ultralearning 
by scott young 




Dedication
To Zorica
Contents
Cover
Title Page
Dedication
Foreword
Chapter I: Can You Get an MIT Education Without Going to MIT?
Chapter II: Why Ultralearning Matters
Chapter III: How to Become an Ultralearner
Chapter IV: Principle 1—Metalearning: First Draw a Map
Chapter V: Principle 2—Focus: Sharpen Your Knife
Chapter VI: Principle 3—Directness: Go Straight Ahead
Chapter VII: Principle 4—Drill: Attack Your Weakest Point
Chapter VIII: Principle 5—Retrieval: Test to Learn
Chapter IX: Principle 6—Feedback: Don’t Dodge the Punches
Chapter X: Principle 7—Retention: Don’t Fill a Leaky Bucket
Chapter XI: Principle 8—Intuition: Dig Deep Before Building Up
Chapter XII: Principle 9—Experimentation: Explore Outside Your
Comfort Zone
Chapter XIII: Your First Ultralearning Project
Chapter XIV: An Unconventional Education
Acknowledgments
Appendix
Notes
Index
About the Author
Copyright
About the Publisher
Foreword
My relationship with Scott Young began in mid-2013. On July 10, I sent him an
email asking if he wanted to set up a call for the following month. We had met at
a conference a few days earlier, and I was hoping he would be willing to
continue the conversation.
“Possibly,” he replied. “I’ll be in Spain then, and the language-learning focus
of my upcoming project may take precedence.”
It wasn’t the response I was hoping for, but it seemed reasonable. Managing
calls while traveling internationally can be tricky, and I understood if he wanted
to wait until he returned. However, I quickly found out that he would not be
returning anytime soon, and it was not the time change nor a spotty internet
connection that would postpone our conversation.
No, it would be hard to catch up with Scott because he was planning to speak
no English for an entire year.
Thus began my introduction to Scott Young and his commitment to
ultralearning. Over the next twelve months, I would trade sporadic emails with
Scott as he traveled to Spain, Brazil, China, and Korea, and proceeded to
become conversational in each of the respective languages along the way. He
was true to his word: it was not until the following summer in 2014 that we
carved out time to catch up regularly and began chatting with each other every
few months.
I was always excited for my calls with Scott—primarily for selfish reasons.
One of my core interests as a writer is the science of how to build good habits
and break bad ones. Someone like Scott, who had so clearly mastered his own
habits, was exactly the type of person who could teach me a thing or two. And
that’s precisely what happened. I can scarcely remember finishing a call with
Scott and not learning something during the previous hour.
Scott and not learning something during the previous hour.
That’s not to say his insight took me by surprise. Scott had already been on
my radar by the time we met at that conference in 2013. He had catapulted to
internet fame one year prior by learning the entire MIT undergraduate computer
science curriculum and passing all of the final tests in less than a year—four
years’ worth of classes in under twelve months. I had seen the TEDx Talk
summarizing his experience, and I read a few of his articles on learning and selfimprovement before tracking him down at the conference.
The idea of taking on an ambitious project—like studying MIT’s
undergraduate curriculum in one year or learning a new language every three
months—is inspirational to many people. I certainly found these bold projects
fascinating. But there was something else about Scott’s projects that resonated
with me on a deeper level: he had a bias toward action.
This is something I have always appreciated about Scott’s approach and
something I believe you will appreciate as a reader of this book. He isn’t focused
on simply soaking up knowledge. He is committed to putting that knowledge to
use. Approaching learning with an intensity and commitment to action is a
hallmark of Scott’s process. This approach speaks to me, in part, because I see
similar patterns in my own life and career. Some of my most meaningful
experiences have been the result of intense self-directed learning.
Although I didn’t know the word ultralearning at the time, one of my first
ultralearning projects was photography. In late 2009, I moved to Scotland for a
few months. It was my first time living abroad, and given the beautiful scenery
throughout the Scottish Highlands, I figured I should buy a decent camera. What
I hadn’t expected, however, was that I would fall in love with the process of
taking photos. What followed was one of the most creative periods of my life.
I learned photography through a variety of methods. I studied the portfolios of
famous photographers. I scouted locations and searched for compelling
perspectives. But, most of all, I learned through one simple method: I took over
100,000 photos that first year. I never enrolled in a photography class. I didn’t
read books on how to become a better photographer. I just committed to
relentless experimentation. This “learning by doing” approach embodies one of
my favorite chapters in this book and Scott’s third principle of ultralearning:
directness.
Directness is the practice of learning by directly doing the thing you want to
learn. Basically, it’s improvement through active practice rather than through
passive learning. The phrases learning something new and practicing something
new may seem similar, but these two methods can produce profoundly different
results. Passive learning creates knowledge. Active practice creates skill.
This is a point that Scott more fully clarifies and refines in chapter 6:
directness leads to skill development. You can research the best instructions on
the bench press technique, but the only way to build strength is to practice lifting
weights. You can read all of the bestselling sales books, but the only way to
actually get customers is to practice making sales calls. Learning can be very
useful, of course, but the danger is that the act of soaking up new facts can be
disconnected from the process of refining a new skill. You can know every fact
about an industry and still lack real-world expertise because you haven’t
practiced the craft.
Scott understands the difficulty of actually learning new skills. I respect him
not only for the quality of his writing but also for the simple fact that he is a
practitioner of his own ideas. I can’t say enough about how important this is: he
has skin in the game. Many ideas sound brilliant on paper but fail in the real
world. As the saying goes, “In theory, there is no difference between theory and
practice. But in practice, there is.”*
As for my photography quest, it didn’t take long for my commitment to direct
practice to pay off. A few months after I bought my camera, I traveled to
Norway and ventured above the Arctic Circle to capture an image of the aurora
borealis. Not long afterward, I was named a finalist for Travel Photographer of
the Year thanks to that image of the Northern Lights. It was a surprising
outcome, but also a testament to how much progress you can make during a
short but intense period of learning.
I never pursued a career as a photographer. It was an ultralearning project I
did for fun and personal satisfaction. But a few years later, right around the time
I first met Scott, I began another period of intense learning with a more
utilitarian outcome in mind: I wanted to be an entrepreneur, and I figured writing
would be one path that could get me there.
Once again, I had selected a domain where I had little formal experience. I
had no entrepreneurs in my family, and I had taken only a single college English
class. But as I read through Ultralearning, I was startled to find that Scott
explained, in nearly step-by-step fashion, the process I followed to go from
unproven entrepreneur to bestselling author.
Principle #1: Metalearning—I started by examining other popular bloggers
and authors. Their methods helped me to create a map for what I needed to do to
become a successful writer.
Principle #2: Focus—I went full-time as a writer nearly from the start. Aside
Principle #2: Focus—I went full-time as a writer nearly from the start. Aside
from a few freelance projects I took on to pay the bills, the vast majority of my
time was spent reading and writing.
Principle #3: Directness—I learned writing by writing. I set a schedule for
myself to write a new article every Monday and Thursday. Over the first two
years, I produced more than 150 essays.
Principle #4: Drill—I systematically broke down each aspect of writing
articles—the headline, the introductory sentence, the transitions, the storytelling,
and more—and put together spreadsheets filled with examples of each segment.
Then I set about testing and refining my ability to perform each small aspect of
the larger task.
Principle #6: Feedback—I personally emailed nearly all of my first ten
thousand subscribers to say hello and to ask for feedback on my writing. It didn’t
scale, but it taught me a lot in the beginning.
. . . and so on.
My point is that Scott’s method works. By following the techniques he lays
out in this book, I was able to build a writing career, create a successful business,
and, ultimately, write a New York Times bestselling book. When I released
Atomic Habits, it was the culmination of years of work centered around the
process of ultralearning.
I think it’s easy to hear stories about writing a bestselling book or learning
four languages in a year and think, “That’s for other people.” I disagree.
Learning something valuable and doing it fast doesn’t have to be confined to
some narrow set of geniuses. It’s a process anyone can embrace. It’s just that
most people never do it because they never had a playbook to show them how.
Until now.
There are good reasons to pursue ultralearning—whether you are conducting a
project for personal or professional interests.
First, deep learning provides a sense of purpose in life. Developing skills is
meaningful. It feels good to get good at something. Ultralearning is a path to
prove to yourself that you have the ability to improve and to make the most of
your life. It gives you the confidence that you can accomplish ambitious things.
Second, deep learning is how you get outsized returns. The simple truth is
most people will never intensely study your area of interest. Doing so—even if
it’s just for a few months—will help you stand out. And once you stand out, you
can get a better job, negotiate for a higher salary or more free time, network with
more interesting people, and otherwise level up your personal and professional
life. Ultralearning helps you develop leverage that you can use elsewhere.
Finally, deep learning is possible. Paul Graham, the famous entrepreneur and
investor, once noted, “In many fields a year of focused work plus caring a lot
would be enough.”* Similarly, I think most people would be surprised by what
they could accomplish with a year (or a few months) of focused learning. The
process of intense self-directed learning can fashion skills you never thought you
could develop. Ultralearning can help you fulfill your potential, and that is
perhaps the best reason of all to pursue it.
The truth is, despite the success of my writing and photography pursuits, these
projects were haphazard. I did them intensely but without guidance or direction.
I made a lot of mistakes. I wish I had this book when I was starting out. I can
only imagine how much wasted time and energy I would have saved.
Ultralearning is a fascinating and inspiring read. Scott has compiled a gold
mine of actionable strategies for learning anything faster. His effort is now your
gain. I hope you enjoy this book as much as I did, and, most important, I hope
you use these ideas to accomplish something ambitious and exciting in your own
life. With the stories and strategies Scott shares in this book, you will have the
knowledge. All that is left is to take action.
—James Clear
Chapter I
Can You Get an MIT Education Without
Going to MIT?
Only a few hours left. I caught myself glancing out the window as the earlymorning light glittered off the buildings in front of me. It was a crisp fall day,
surprisingly sunny for a famously rainy city. Well-dressed men carried
briefcases and fashionable women pulled miniature dogs beneath my eleventhstory vantage point. Buses dragged reluctant commuters into town one last time
before the weekend. The city might have been rousing from its slumber, but I
had been awake since before dawn.
Now is not the time for daydreaming, I reminded myself and shifted my
attention back to the half-finished math problems scribbled on the notebook in
front of me. “Show that for any finite part of the unit sphere . . .” the problem
began. The class was Multivariate Calculus for the Massachusetts Institute of
Technology. The final exam would start soon, and I had little time left to
prepare. What was curl, again . . . ? I closed my eyes and tried to form a picture
of the problem in my head. There’s a sphere. I know that. I conjured a bright red
ball in my mind’s eye, floating in empty space. Now what was n̂? The n̂stands
for normal, I reminded myself, meaning an arrow that points straight up from the
surface. My red ball became furry, with hairlike vectors standing straight at all
ends. But what about curl? My imagination turned to waves of tiny arrows
pulsating in a vast sea. Curl marked the eddies, swirling around in little loops. I
thought again to my furry, red ball with the static-charged hairdo. My fuzzy
sphere had no whorls, so there must not be any curl, I reasoned. But how do I
prove it? I scratched down some equations. Better double-check it. My mental
pictures were clear, but my symbol manipulation was a lot sloppier. There
wasn’t much time left, and every second of preparation counted. I needed to
grind through as many problems as possible before time ran out.
That was nothing unusual for an MIT student. Tricky equations, abstract
concepts, and difficult proofs are all a normal part of one of the most prestigious
educations in math and science in the world. Except that I was not an MIT
student. In fact, I had never even been to Massachusetts. All of this was taking
place in my bedroom, twenty-five hundred miles away in Vancouver, Canada.
And although an MIT student typically covers the entirety of multivariate
calculus over a semester, I had started only five days before.
The MIT Challenge
I have never attended MIT. Instead, my college days were spent studying
business at the University of Manitoba, a middle-ranked Canadian school I could
actually afford. After graduating with a bachelor of commerce, I felt as though I
had picked the wrong major. I wanted to be an entrepreneur and so had studied
business, thinking that would be the best route to becoming my own boss. Four
years later, I discovered that a business major was largely a finishing school for
entrants into the world of big corporations, gray suits, and standard operating
procedures. Computer science, in contrast, was a major where you actually
learned to make things. Programs, websites, algorithms, and artificial
intelligence were what had interested me in entrepreneurship in the first place,
and I was struggling to decide what to do about it.
I could go back to school, I thought. Enroll again. Spend another four years
working toward a second degree. But taking out student loans and giving up a
half decade of my life to repeat the bureaucracy and rules of college didn’t seem
very appealing. There had to be a better way to learn what I wanted.
Around that time, I stumbled across a class taught at MIT and posted online. It
had fully recorded lectures, assignments, and quizzes; even the actual exams
used in the real class with the solution keys were provided. I decided to try
taking the class. To my surprise, I found that the class was much better than
most of the classes I had paid thousands of dollars to attend in university. The
lectures were polished, the professor was engaging, and the material was
fascinating. Digging further, I could see that this wasn’t the only class MIT
offered for free. MIT had uploaded the materials from hundreds of different
offered for free. MIT had uploaded the materials from hundreds of different
classes. I wondered if this could be the solution to my problem. If anyone could
learn the content of an MIT class for free, would it be possible to learn the
content of an entire degree?
Thus began almost six months of intense research into a project I named the
MIT Challenge. I looked up the actual MIT curriculum for computer science
undergrads. I matched and compared the list with the resources MIT offered
online. Unfortunately, that was a lot easier said than done. MIT’s
OpenCourseWare, the platform used for uploading class material, had never
been intended as a substitute for attending the school. Some classes simply
weren’t offered and needed to be swapped out. Others had such scant material
that I wondered if they would even be possible to complete. Computation
Structures, one of the required courses, which taught how to build a computer
from scratch using circuits and transistors, had no recorded lectures or assigned
textbook. To learn the class content, I would have to decipher abstract symbols
written on a slideshow meant to accompany the lecture. Missing materials and
ambiguous evaluation criteria meant that doing every class exactly as an MIT
student would was out of the question. However, a simpler approach might
work: just try to pass the final exams.
This focus on final exams later expanded to include programming projects for
the classes that had them. These two criteria formed the skeleton of an MIT
degree, covering most of the knowledge and skills I wanted to learn, with none
of the frills. No mandatory attendance policy. No due dates on assignments. The
final exams could be taken whenever I was ready and retaken with an alternate
exam if I happened to fail one. Suddenly what had initially seemed like a
disadvantage—not having physical access to MIT—became an advantage. I
could approximate the education of an MIT student for a fraction of the cost,
time, and constraints.
Exploring this possibility further, I even did a test class using the new
approach. Instead of showing up to prescheduled lectures, I watched downloaded
videos for the class at twice the normal speed. Instead of meticulously doing
each assignment and waiting weeks to learn my results, I could test myself on
the material one question at a time, quickly learning from my mistakes. Using
these and other methods, I found I could scrape through a class in as little as a
week’s time. Doing some quick calculations and adding some room for error, I
decided it might be possible to tackle the remaining thirty-two classes in under a
year.
Although it began as a personal quest, I started to see that there were bigger
Although it began as a personal quest, I started to see that there were bigger
implications beyond my little project. Technology has made learning easier than
ever, yet tuition costs are exploding. A four-year degree used to be an assurance
of a decent job. Now it is barely a foot in the door. The best careers demand
sophisticated skills that you’re unlikely to stumble upon by chance. Not just
programmers but managers, entrepreneurs, designers, doctors, and nearly every
other profession is rapidly accelerating the knowledge and skills required, and
many are struggling to keep up. In the back of my mind, I was interested not
only in computer science but in seeing if there might be a new way to master the
skills needed in work and life.
As my attention drifted once more to the scene developing outside my
window, I thought about how all this had started. I thought about how I wouldn’t
be attempting my odd little experiment at all had it not been for a chance
encounter with an intense, teetotaling Irishman on another continent almost three
years earlier.
Fluent in Three Months?
“My problem isn’t with the French—just Parisians,” Benny Lewis vented to me
in an Italian restaurant in the heart of Paris. Lewis was vegetarian, not always
easy to accommodate in a country famous for steak tartare and foie gras. Eating
a plate of penne arrabbiata, a favorite he had picked up while working in a youth
hostel in Italy, Lewis spoke in fluent French, not minding much if any of the
locals overheard his complaints. His discontent stemmed from a particularly
dreary year working as a stagiaire in an engineering firm in Paris. He had found
it hard to adjust to the notorious job demands and social life in France’s biggest
city. Still, he thought, perhaps he shouldn’t be too critical. It was that experience,
after all, that had led him to leave his life as an engineer and travel around the
world learning languages.
I had been introduced to Lewis during a moment of personal frustration. I was
living in France as part of a student exchange program. I had left home with high
hopes of ending the year speaking effortless French, but things didn’t seem to be
turning out that way. Most of my friends spoke to me in English, including the
French ones, and it was starting to feel as though one year wouldn’t be enough.
I complained about this state of affairs to a friend from home; he told me
about a guy he had heard of who traveled from country to country, challenging
himself to learn a language in three months. “Bullshit,” I said, with more than a
hint of envy. Here I had been struggling to chat with people after months of
hint of envy. Here I had been struggling to chat with people after months of
immersion, and this guy was challenging himself to do so after only three
months. Despite my skepticism, I knew I needed to meet Lewis to see if he
understood something about learning languages that I didn’t. An email and a
train ride later, and Lewis and I were meeting face-to-face.
“Always have a challenge,” Lewis told me as he continued with his life
advice, now guiding me on a postlunch tour of central Paris: Lewis’s earlier
feelings about Paris were starting to soften, and as we walked from the Notre
Dame to the Louvre, his mood turned nostalgic about his days in the city. His
strong opinions and passions, I would later learn, not only fueled his desire to
take on ambitious challenges but could also get him into trouble. He was once
detained by Brazilian federal police after an immigration officer overheard him
cursing her in Portuguese to friends outside when she had denied him a visa
extension. The irony was that his visa had been denied because she didn’t
believe his Portuguese could be so good from such a short stay, and she
suspected him of secretly trying to immigrate to Brazil outside the terms of his
tourist visa.
As we continued to walk, now on the grounds in front of the Eiffel Tower,
Lewis explained his approach: Start speaking the very first day. Don’t be afraid
to talk to strangers. Use a phrasebook to get started; save formal study for later.
Use visual mnemonics to memorize vocabulary. What struck me were not the
methods but the boldness with which he applied them. While I had timidly been
trying to pick up some French, worrying about saying the wrong things and
being embarrassed by my insufficient vocabulary, Lewis was fearless, diving
straight into conversations and setting seemingly impossible challenges for
himself.
That approach had served him well. He was already fluent in Spanish, Italian,
Gaelic, French, Portuguese, Esperanto, and English and had recently reached a
conversational level while staying in the Czech Republic for three months. But it
was his newest challenge he was planning that intrigued me the most: fluency in
German after just three months.
It wasn’t, strictly speaking, Lewis’s first time with German. He had taken
German classes for five years in high school and had briefly visited Germany
twice before. However, like many of the students who spent time learning a
language in school, he still couldn’t speak it. He admitted with embarrassment,
“I couldn’t even order breakfast in German if I wanted to.” Still, the unused
knowledge built up from classes taken over a decade earlier would probably
make his challenge easier than starting from scratch. To compensate for the
make his challenge easier than starting from scratch. To compensate for the
reduced difficulty, Lewis decided to raise the stakes.
Normally, he challenged himself to reach the equivalent of a B2 level in a
language after three months. The B2 level—the fourth out of six levels
beginning A1, A2, B1, and so on—is described by the Common European
Framework of Reference for Languages (CEFR) as upper intermediate, allowing
the speaker to “interact with a degree of fluency and spontaneity that makes
regular interaction with native speakers quite possible without strain for either
party.” However, for his German challenge, Lewis decided to go for the highest
exam level offered: C2. This level represents a complete mastery of the
language. To reach a C2 level, the learner must “understand with ease virtually
everything heard or read” and “express themselves spontaneously, very fluently
and precisely, differentiating finer shades of meaning even in the most complex
situations.” The Goethe-Institut, which administers the exam, recommends at
least 750 hours of instruction, not including extensive practice outside the
classroom, to reach this benchmark.
1
A few months later, I heard back from Lewis about his project. He had missed
his goal of passing the C2 exam by a hair. He had passed four of five criteria for
his exam but had failed the listening comprehension section. “I spent too much
time listening to the radio,” he chastised himself. “I should have done more
active listening practice.” Fluency in three months of intensive practice had
eluded him, although he had come surprisingly close. In the seven years after my
first encounter with the Irish polyglot, he has gone on to attempt his three-month
challenge in half a dozen more countries, adding to his linguistic repertoire some
Arabic, Hungarian, Mandarin Chinese, Thai, American Sign Language, and even
Klingon (the invented Star Trek language).
What I didn’t realize at the time but understand now was that Lewis’s
accomplishments weren’t all that rare. In the space of linguistic feats alone, I
have encountered hyperpolyglots who speak forty-plus languages, adventureranthropologists who can start speaking previously unknown languages after a
few hours of exposure, and many other travelers, like Lewis, who hop from
tourist visa to tourist visa, mastering new languages. I also saw that this
phenomenon of aggressive self-education with incredible results wasn’t
restricted to languages alone.
How Roger Craig Gamed Jeopardy!
“What is The Bridge on the River Kwai?” Roger Craig hastily scribbled the
question on his screen. Despite first fumbling over the legibility of the film
title’s final word, Craig was correct. He had won $77,000—the highest singleday winnings in Jeopardy! history at the time. Craig’s victory wasn’t a fluke. He
broke records again, amassing nearly two hundred thousand dollars, the highest
ever five-game winning streak. Such a feat would be remarkable on its own, but
what was more incredible was how he did it. Reflecting on the moment, Craig
says, “My first thought wasn’t ‘Wow, I just won seventy-seven thousand
dollars.’ It was ‘Whoa, my site really worked.’”
2
How do you study for a test that can ask any question? That was the essential
problem Craig faced as he prepared to compete. Jeopardy! is famous for
stumping home audiences with trivia questions that can ask about anything from
Danish kings to Damocles. Thus the great champions of Jeopardy! tend to be
brainy know-it-alls who have spent a lifetime amassing the huge library of
factual knowledge needed to spit out answers on any topic. Studying for
Jeopardy! might feel like an impossible task, as you would need to study almost
every conceivable subject. Craig’s solution, however, was to rethink the process
of acquiring knowledge itself. To do that, he built a website.
“Everybody that wants to succeed at a game is going to practice the game,”
Craig contends. “You can practice haphazardly, or you can practice efficiently.”
3
To amass the wide-ranging trivia needed to break records, he decided to be
ruthlessly analytical about how he acquired knowledge. A computer scientist by
trade, he decided to start off by downloading the tens of thousands of questions
and answers from every Jeopardy! game ever aired. He tested himself on those
during his free time for months, and then, as it became clear that he was going to
go on television, he switched to aggressively quizzing himself on the questions
full-time. He then applied text-mining software to categorize the questions into
different topics, such as art history, fashion, and science. He used data
visualization to map out his strengths and weaknesses. The text-mining software
separated the different topics, which he visualized as different circles. The
position of any given circle on his graph showed how good he was in that topic
—higher meant he knew more about that topic. The size of the circle indicated
how frequent that topic was. Bigger circles were more common and thus better
choices for further study. Beneath the variety and randomness in the show, he
started to uncover hidden patterns. Some clues in the show are “Daily Doubles,”
which allow a contestant to double his or her score, or lose it all. These
extremely valuable clues may seem randomly placed, but having the entire
Jeopardy! archives at his fingertips, Craig found that their position followed
trends. One could hunt out the valuable Doubles by hopping between categories
and focusing on high-point clues, breaking the conventional approach to the
show of sticking within a single category until it was completed.
Craig also found trends within the types of questions asked. Although
Jeopardy! could conceivably ask questions on any topic, the format of the game
is designed to entertain a home audience, not to challenge competitors.
Following this reasoning, Craig found that he could get away with studying the
best-known trivia within a category, rather than digging deep into any particular
direction. If a subject was specialized, he knew the answers would be geared
toward the best-known examples. By analyzing his own weakness on archival
questions, he could see which topics he needed to study more to be competitive.
For example, he found that he was weak on fashion and focused on studying that
topic more deeply.
Using analytics to figure out what to study was only the first step. From there,
Craig employed spaced-repetition software to maximize his efficiency. Spacedrepetition software is an advanced flash card algorithm first developed by the
Polish researcher Piotr Woźniak in the 1980s.
4 Woźniak’s algorithm was
designed to optimally time when you need to review material in order to
remember it. Given a large database of facts, most people will forget what they
learn first, needing to remind themselves of it again and again for it to stick. The
algorithm fixes this problem by calculating the optimal time for reviewing each
fact so you don’t waste energy overdrilling the same information, but also so you
don’t forget what you’ve already learned. This tool allowed Craig to efficiently
memorize the thousands of facts he would need for his later victory.
Although the show airs only one episode per day, Jeopardy! tapes five
episodes at a time. Craig was coming back to his hotel room after winning five
games straight, and he couldn’t sleep. He said, “You can simulate the game, but
you can’t simulate winning two hundred thousand dollars in five hours and
setting the single-day record on a game show you’ve wanted to be on since you
were twelve years old.”
5 Combining unorthodox tactics and aggressive analysis,
he had gamed the game show and won.
Roger Craig wasn’t the only person I found who had seen his fortunes change
as a result of aggressive self-education. I didn’t know it at the time, but in 2011,
the same year my MIT Challenge would begin, Eric Barone was starting his own
obsession. Unlike mine, however, his efforts would extend for nearly five years
and require mastering many completely different skills.
From Minimum Wage to Millionaire
Eric Barone had just graduated from the University of Washington Tacoma with
a degree in computer science when he thought, Now’s my chance. He had
decided that he wanted to make his own video games and that now, before he got
comfortable in a salaried programming job, was his opportunity to do something
about it. He already had his inspiration. He wanted his game to pay homage to
Harvest Moon, a charming Japanese series of games in which the player must
build a successful farm: grow crops, raise animals, explore the countryside, and
form relationships with other villagers. “I loved that game,” he said about his
childhood experience with the title. “But it could have been so much better.” He
knew that if he didn’t follow through with his own vision, that improved version
would never be a reality.
Developing a commercially successful video game isn’t easy. AAA game
companies budget hundreds of millions of dollars and employ thousands of
people on their top titles. The talent required is similarly broad. Game
development requires programming, visual art, musical composition, story
writing, game design, and dozens more skills, depending on the genre and style
of game developed. The breadth of skills required makes game development
much harder for smaller teams than other art forms such as music, writing, or
visual arts. Even highly talented independent game developers generally have to
collaborate with a few people to span all the skills required. Eric Barone,
however, decided to work on his game entirely alone.
Deciding to work alone came from a personal commitment to his vision and
an indefatigable self-confidence that he could finish the game. “I like to have
complete control over my own vision,” he explained, saying that it might have
been “impossible to find people who were on the same page” regarding the
design. However, that choice meant that he would need to become proficient in
game programming, music composition, pixel art, sound design, and story
writing. More than just a game design project, Barone’s odyssey would entail
mastering each aspect of game design itself.
Pixel art was Barone’s biggest weakness. This style of art harkens back to the
earlier era of video games when rendering graphics was difficult to do on slow
computers. Pixel art is not done with fluid lines or photorealistic textures.
Instead, a compelling image must be created by placing pixels, the colored dots
that make up computer graphics, one at a time—painstaking and difficult work.
A pixel artist must convey movement, emotion, and life from a grid of colored
squares. Barone liked to doodle and draw, but that didn’t prepare him for the
squares. Barone liked to doodle and draw, but that didn’t prepare him for the
difficulty. He had to learn this skill “completely from scratch.” Getting his art
skills to a commercial level wasn’t easy. “I must have done most of the artwork
three to five times over,” he said. “For the character portraits, I did those at least
ten times.”
Barone’s strategy was simple but effective. He practiced by working directly
on the graphics he wanted to use in his game. He critiqued his own work and
compared it to art he admired. “I tried to break it down scientifically,” he
explained. “I would ask myself, ‘Why do I like this? Why don’t I like that?’”
when looking at other artists’ work. He supplemented his own practice by
reading about pixel art theory and finding tutorials that could fill gaps in his
knowledge. When he encountered a difficulty in his art, he broke it down: “I
asked, ‘What goal do I want to reach?’ and then ‘How might I get there?’” At
some point in his work on the game, he felt his colors were too dull and boring.
“I wanted the colors to pop,” he said. So he researched color theory and
intensively studied other artists to see how they used colors to make things
visually interesting.
Pixel art was just a single aspect Barone had to learn. He also composed all of
the music for his game, redoing it from scratch more than once to make sure it
met his high expectations. Whole sections of the game mechanics were
developed and scrapped when they failed to meet his rigorous standards. This
process of practicing directly and redoing things allowed him to get steadily
better at all of the aspects of game design. Although it lengthened the time it
took to complete the game, it also enabled his finished product to compete with
games created by an army of specialized artists, programmers, and composers.
Throughout the five-year development process, Barone avoided seeking
employment as a computer programmer. “I didn’t want to get involved in
something substantial,” he said. “I wouldn’t have had the time, and I wanted to
give game development my best shot.” Instead, he worked as a theater usher,
earning minimum wage so that he wouldn’t get distracted. His meager earnings
from his job, combined with support from his girlfriend, allowed Barone to get
by as he focused on his passion.
That passion and dedication to mastery paid off. Barone released Stardew
Valley in February 2016. The game quickly became a surprise hit, outselling
many of the big-studio titles offered on the computer game platform Steam.
Across multiple platforms, Barone estimates that within the first year of its
release, Stardew Valley had sold well over 3 million copies. In months, he went
from an unknown designer earning minimum wage to a millionaire named one
of Forbes’ “30 Under 30” stars within game development. His dedication to
mastering the skills involved played no small part in that success. Destructoid, in
its review of Stardew Valley, described the artwork as “incredibly endearing and
beautiful.”
6 Barone’s commitment to his vision and aggressive self-education
had paid off handsomely.
The MIT Challenge and Beyond
Back in my cramped apartment, I was grading my calculus exam. It was tough,
but it looked as though I had passed. I was relieved, but it wasn’t a time to relax.
Next Monday, I would be starting all over again, with a new course, and I still
had almost a year to go.
As the calendar changed, so did my strategies. I switched from trying to do a
single class in several days to spending a month doing three to four classes in
parallel. I hoped that would spread the learning over a longer period of time and
reduce some of the negative effects of cramming. As I made more progress, I
also slowed down. My first few classes were done with aggressive haste so I
could stay on schedule to meet my self-imposed deadline. After it seemed likely
that I could finish, I was able to shift from studying sixty hours per week to
studying thirty-five to forty. Finally, in September 2012, less than twelve months
after I had begun, I finished the final class.
Completing the project was eye-opening for me. For years, I had thought the
only way to learn things deeply was to push through school. Finishing this
project taught me not only that this assumption was false but that this alternate
path could be more fun and exciting. In university, I had often felt stifled, trying
to stay awake during boring lectures, grinding through busywork assignments,
forcing myself to learn things I had no interest in just to get the grade. Because
this project was my own vision and design, it rarely felt painful, even if it was
often challenging. The subjects felt alive and exciting, rather than stale chores to
be completed. For the first time ever, I felt I could learn anything I wanted to
with the right plan and effort. The possibilities were endless, and my mind was
already turning toward learning something new.
Then I got a message from a friend: “You’re on the front page of Reddit, you
know.” The internet had found my project, and it was generating quite a
discussion. Some liked the idea but doubted its usefulness: “It’s sad that
employers won’t really treat this in the same way as a degree, even if he has the
same amount (or more) knowledge than a graduate does.” One user claiming to
be the head of R&D for a software company disagreed: “This is the type of
person I want. I really do not care if you have a degree or not.”
7 The debate
raged. Had I actually done it or not? Would I be able to get a job as a
programmer after this? Why try to do this in a year? Was I crazy?
The initial surge of attention led to other requests. An employee at Microsoft
wanted to set me up for a job interview. A new startup asked me to join its team.
A publishing house in China offered me a book deal to share some studying tips
with beleaguered Chinese students. However, those weren’t the reasons I had
done the project. I was already happy working as a writer online, which had
supported me financially throughout my project and would continue to do so
afterward. My goal for the project wasn’t to get a job but to see what was
possible. After just a few months of finishing my first big project, ideas for new
ones were already bubbling up inside my head.
I thought of Benny Lewis, my first example in this strange world of intense
self-education. Following his advice, I had eventually reached an intermediate
level of French. It had been hard work, and I was proud that I had been able to
push against my initial difficulty of being surrounded by a bubble of English
speakers to learn enough French to get by. However, after finishing my MIT
experience, I was injected with a new confidence I hadn’t had in France. What if
I didn’t make the mistake I made last time? What if, instead of forming a group
of English-speaking friends and struggling to pop out of that bubble once my
French was good enough, I emulated Benny Lewis and dived straight into
immersion from the very first day? How much better could I be, if as in my MIT
Challenge, I held nothing back and optimized everything around learning a new
language as intensely and effectively as possible?
As luck would have it, around that time my roommate was planning on going
back to grad school and wanted some time off to travel first. We’d both been
saving, and if we pooled our resources and were frugal in how we planned our
trip, we figured we might be able to do something exciting. I told him about my
experiences in France, both of learning French and of secretly believing that
much more was possible. I told him about the social bubble that had formed
when I had arrived without speaking the language and how difficult it had been
to break out of it later. What if, instead of just hoping you’d practice enough,
you don’t give yourself an escape route? What if you commit to speaking only
the language you’re trying to learn from the first moment you step off the plane?
My friend was skeptical. He had seen me study MIT classes for a year from
My friend was skeptical. He had seen me study MIT classes for a year from
across our apartment. My sanity was still an open question, but he wasn’t as
confident in his own ability. He wasn’t sure he could do it, although he was
willing to give it a shot, as long as I didn’t have any expectations of him to
succeed.
That project, which my friend and I titled “The Year Without English,” was
simple. We’d go to four countries, three months each. The plan in each country
was straightforward: no speaking English, either with each other or with anyone
we’d meet, from the first day. From there we’d see how much we could learn
before our tourist visas ran out and we were pushed to a new destination.
Our first stop was Valencia, Spain. We had just landed in the airport when we
encountered our first obstacle. Two attractive British girls came up to us, asking
for directions. We looked at each other and awkwardly sputtered out the little
Spanish we knew, pretending we didn’t speak any English. They didn’t
understand us and asked us again, now in an exasperated tone. We stumbled
over some more Spanish and, believing we couldn’t speak English, they walked
away in frustration. Already, it seemed, not speaking English was having
unintended consequences. Despite that inauspicious beginning, our Spanish
ability grew even faster than I had anticipated. After two months in Spain, we
were interacting in Spanish beyond what I had achieved in an entire year of
partial immersion in France. We would go to our tutor in the morning, study a
little at home, and spend the rest of the day hanging out with friends, chatting at
restaurants, and soaking up the Spanish sun. My friend, despite his earlier
doubts, was also a convert to this new approach to learning things. Although he
didn’t care to study grammar and vocabulary as aggressively as I did, by the end
of our stay, he too was integrating seamlessly into life in Spain. The method
worked far better than we had hoped, and we were now believers.
We continued the trip, going to Brazil to learn Portuguese, China to learn
Mandarin, and South Korea to learn Korean. Asia proved a far harder task than
Spain or Brazil. In our preparation, we had assumed those languages would be
only a little more difficult than the European ones, although it turned out that
they were much harder. As a result, our no-English rule was starting to crack,
although we still applied it as much as we could. Even if our Mandarin and
Korean didn’t reach the same level of ability after a short stay, it was still
enough to make friends, travel, and converse with people on a variety of topics.
At the end of our year, we could confidently say we spoke four new languages.
Having seen the same approach work for academic computer science and
language-learning adventures, I was slowly becoming convinced that it could be
language-learning adventures, I was slowly becoming convinced that it could be
applied to much more. I had enjoyed drawing as a kid, but like most people’s
attempts, any faces I drew looked awkward and artificial. I had always admired
people who could quickly sketch a likeness, whether it be street-side caricaturists
to professional portrait painters. I wondered if the same approach to learning
MIT classes and languages could also apply to art.
I decided to spend a month improving my ability to draw faces. My main
difficulty, I realized, was in placing the facial features properly. A common
mistake when drawing faces, for instance, is putting the eyes too far up the head.
Most people think they sit in the top two-thirds of the head. In truth, they’re
more typically halfway between the top of the head and the chin. To overcome
these and other biases, I did sketches based on pictures. Then I would take a
photo of the sketch with my phone and overlay the original image on top of my
drawing. Making the photo semitransparent allowed me to see immediately
whether the head was too narrow or wide, the lips too low or too high or whether
I had put the eyes in the right spot. I did this hundreds of times, employing the
same rapid feedback strategies that had served me well with MIT classes.
Applying this and other strategies, I was able to get a lot better at drawing
portraits in a short period of time (see below).
Uncovering the Ultralearners
On the surface, projects such as Benny Lewis’s linguistic adventures, Roger
Craig’s trivia mastery, and Eric Barone’s game development odyssey are quite
different. However, they represent instances of a more general phenomenon I
call ultralearning.* As I dug deeper, I found more stories. Although they
differed in the specifics of what had been learned and why, they shared a
common thread of pursuing extreme, self-directed learning projects and
employed similar tactics to complete them successfully.
Steve Pavlina is an ultralearner. By optimizing his university schedule, he
took a triple course load and completed a computer science degree in three
semesters. Pavlina’s challenge long predated my own experiment with MIT
courses and was one of the first inspirations that showed me compressing
learning time might be possible. Done without the benefit of free online classes,
however, Pavlina attended California State University, Northridge, and
graduated with actual degrees in computer science and mathematics.
8
Diana Jaunzeikare embarked on an ultralearning project to replicate a PhD in
computational linguistics.
9 Benchmarking Carnegie Mellon University’s
doctoral program, she wanted to not only take classes but also conduct original
research. Her project had started because going back to academia to get a real
doctorate would have meant leaving the job she loved at Google. Like many
other ultralearners before her, Jaunzeikare’s project was an attempt to fill a gap
in education when formal alternatives didn’t fit with her lifestyle.
Facilitated by online communities, many ultralearners operate anonymously,
their efforts observable only by unverifiable forum postings. One such poster at
Chinese-forums.com, who goes only by the username Tamu, extensively
documented his process of studying Chinese from scratch. Devoting “70–80+
hours each week” over four months, he challenged himself to pass the HSK 5,
China’s second highest Mandarin proficiency exam.
10
Other ultralearners shed the conventional structures of exams and degrees
altogether. Trent Fowler, starting in early 2016, embarked on a yearlong effort to
become proficient in engineering and mathematics.
11 He titled it the STEMpunk
Project, a play on the STEM fields of science, technology, engineering, and
mathematics he wanted to cover and the retrofuturistic steampunk aesthetic.
Fowler split his project into modules. Each module covered a particular topic,
including computation, robotics, artificial intelligence, and engineering, but was
driven by hands-on projects instead of copying formal classes.
Every ultralearner I encountered was unique. Some, like Tamu, preferred
punishing, full-time schedules to meet harsh, self-imposed deadlines. Others,
like Jaunzeikare, managed their projects on the side while maintaining full-time
jobs and work obligations. Some aimed at the recognizable benchmarks of
standardized exams, formal curricula, and winning competitions. Others
standardized exams, formal curricula, and winning competitions. Others
designed projects that defied comparison. Some specialized, focusing
exclusively on languages or programming. Others desired to be true polymaths,
picking up a highly varied set of skills.
Despite their idiosyncrasies, the ultralearners had a lot of shared traits. They
usually worked alone, often toiling for months and years without much more
than a blog entry to announce their efforts. Their interests tended toward
obsession. They were aggressive about optimizing their strategies, fiercely
debating the merits of esoteric concepts such as interleaving practice, leech
thresholds, or keyword mnemonics. Above all, they cared about learning. Their
motivation to learn pushed them to tackle intense projects, even if it often came
at the sacrifice of credentials or conformity.
The ultralearners I met were often unaware of one another. In writing this
book, I wanted to bring together the common principles I observed in their
unique projects and in my own. I wanted to strip away all the superficial
differences and strange idiosyncrasies and see what learning advice remains. I
also wanted to generalize from their extreme examples something an ordinary
student or professional can find useful. Even if you’re not ready to tackle
something as extreme as the projects I’ve described, there are still places where
you can adjust your approach based on the experience of ultralearners and
backed by the research from cognitive science.
Although the ultralearners are an extreme group of people, this approach to
things holds potential for normal professionals and students. What if you could
create a project to quickly learn the skills to transition to a new role, project, or
even profession? What if you could master an important skill for your work, as
Eric Barone did? What if you could be knowledgeable about a wide variety of
topics, like Roger Craig? What if you could learn a new language, simulate a
university degree program, or become good at something that seems impossible
to you right now?
Ultralearning isn’t easy. It’s hard and frustrating and requires stretching
outside the limits of where you feel comfortable. However, the things you can
accomplish make it worth the effort. Let’s spend a moment trying to see what
exactly ultralearning is and how it differs from the most common approaches to
learning and education. Then we can examine what the principles are that
underlie all learning, to see how ultralearners exploit them to learn faster.
Chapter II
Why Ultralearning Matters
What exactly is ultralearning? While my introduction to the eclectic group of
intense autodidacts started with seeing examples of unusual learning feats, to go
forward we need something more concise. Here’s an imperfect definition:
Ultralearning: A strategy for acquiring skills and knowledge that is both
self-directed and intense.
First, ultralearning is a strategy. A strategy is not the only solution to a given
problem, but it may be a good one. Strategies also tend to be well suited for
certain situations and not others, so using them is a choice, not a commandment.
Second, ultralearning is self-directed. It’s about how you make decisions
about what to learn and why. It’s possible to be a completely self-directed
learner and still decide that attending a particular school is the best way to learn
something. Similarly, you could “teach yourself” something on your own by
mindlessly following the steps outlined in a textbook. Self-direction is about
who is in the driver’s seat for the project, not about where it takes place.
Finally, ultralearning is intense. All of the ultralearners I met took unusual
steps to maximize their effectiveness in learning. Fearlessly attempting to speak
a new language you’ve just started to practice, systematically drilling tens of
thousands of trivia questions, and iterating through art again and again until it is
perfect is hard mental work. It can feel as though your mind is at its limit. The
opposite of this is learning optimized for fun or convenience: choosing a
language-learning app because it’s entertaining, passively watching trivia show
reruns on television so you don’t feel stupid, or dabbling instead of serious
reruns on television so you don’t feel stupid, or dabbling instead of serious
practice. An intense method might also produce a pleasurable state of flow, in
which the experience of challenge absorbs your focus and you lose track of time.
However, with ultralearning, deeply and effectively learning things is always the
main priority.
This definition covers the examples I’ve discussed so far, but in some ways it
is unsatisfyingly broad. The ultralearners I’ve met have a lot more overlapping
qualities than this minimal definition implies. This is why in the second part of
the book I’ll discuss deeper principles that are common in ultralearning and how
they can enable some impressive achievements. Before that, however, I want to
explain why I think ultralearning matters—because although the examples of
ultralearning may seem eccentric, the benefits of this approach to learning are
deep and practical.
The Case for Ultralearning
It’s obvious that ultralearning isn’t easy. You’ll have to set aside time from your
busy schedule in order to pursue something that will strain you mentally,
emotionally, and possibly even physically. You’ll be forced to face down
frustrations directly without retreating into more comfortable options. Given this
difficulty, I think it’s important to articulate clearly why ultralearning is
something you should seriously consider.
The first reason is for your work. You already expend much of your energy
working to earn a living. In comparison, ultralearning is a small investment,
even if you went so far as to temporarily make it a full-time commitment.
However, rapidly learning hard skills can have a greater impact than years of
mediocre striving on the job. Whether you want to change careers, take on new
challenges, or accelerate your progress, ultralearning is a powerful tool.
The second reason is for your personal life. How many of us have dreams of
playing an instrument, speaking a foreign language, becoming a chef, writer, or
photographer? Your deepest moments of happiness don’t come from doing easy
things; they come from realizing your potential and overcoming your own
limiting beliefs about yourself. Ultralearning offers a path to master those things
that will bring you deep satisfaction and self-confidence.
Although the motivation behind ultralearning is timeless, let’s start by looking
at why investing in mastering the art of learning hard things quickly is going to
become even more important to your future.
Economics: Average Is Over
In the words of the economist Tyler Cowen, “Average is over.”
1
In his book of
the same title, Cowen argues that because of increased computerization,
automation, outsourcing, and regionalization, we are increasingly living in a
world in which the top performers do a lot better than the rest.
Driving this effect is what is known as “skill polarization.” It’s well known
that income inequality has been increasing in the United States over the last
several decades. However, this description ignores a more subtle picture. The
MIT economist David Autor has shown that instead of inequality rising across
the board, there are actually two different effects: inequality rising at the top and
lowering at the bottom.
2 This matches Cowen’s thesis of average being over,
with the middle part of the income spectrum being compressed into the bottom
and stretched out at the top. Autor identifies the role that technology has had in
creating this effect. The advance of computerization and automation
technologies has meant that many medium-skilled jobs—clerks, travel agents,
bookkeepers, and factory workers—have been replaced with new technologies.
New jobs have arisen in their place, but those jobs are often one of two types:
either they are high-skilled jobs, such as engineers, programmers, managers, and
designers, or they are lower-skilled jobs such as retail workers, cleaners, or
customer service agents.
Exacerbating the trends caused by computers and robots are globalization and
regionalization. As medium-skilled technical work is outsourced to workers in
developing nations, many of those jobs are disappearing at home. Lower-skilled
jobs, which often require face-to-face contact or social knowledge in the form of
cultural or language abilities, are likely to remain. Higher-skilled work is also
more resistant to shipping overseas because of the benefits of coordination with
management and the market. Think of Apple’s tagline on all of its iPhones:
“Designed in California. Made in China.” Design and management stay;
manufacturing goes. Regionalization is a further extension of this effect, with
certain high-performing companies and cities making outsized impacts on the
economy. Superstar cities such as Hong Kong, New York, and San Francisco
have dominating effects on the economy as firms and talent cluster together to
take advantage of proximity.
This paints a picture that might either be bleak or hopeful, depending on your
response to it. Bleak, because it means that many of the assumptions embedded
in our culture about what is necessary to live a successful, middle-class lifestyle
are quickly eroding. With the disappearance of medium-skilled jobs, it’s not
enough to get a basic education and work hard every day in order to succeed.
Instead, you need to move into the higher-skilled category, where learning is
constant, or you’ll be pushed into the lower-skilled category at the bottom.
Underneath this unsettling picture, however, there is also hope. Because if you
can master the personal tools to learn new skills quickly and effectively, you can
compete more successfully in this new environment. That the economic
landscape is changing may not be a choice any of us has control over, but we can
engineer our response to it by aggressively learning the hard skills we need to
thrive.
Education: Tuition Is Too High
The accelerating demand for high-skilled work has increased the demand for
college education. Except instead of expanding into education for all, college has
become a crushing burden, with skyrocketing tuition costs making decades of
debt a new normal for graduates. Tuition has increased far faster than the rate of
inflation, which means that unless you are well poised to translate that education
into a major salary increase, it may not be worth the expense.
3
Many of the best schools and institutions fail to teach many of the core
vocational skills needed to succeed in the new high-skilled jobs. Although higher
education has traditionally been a place where minds were shaped and characters
developed, those lofty goals seem increasingly out of touch with the basic
financial realities facing new graduates. Therefore, even for those who do go to
college, there are very often skill gaps between what was learned in school and
what is needed to succeed. Ultralearning can fill some of those gaps when going
back to school isn’t an affordable option.
Rapidly changing fields also mean that professionals need to constantly learn
new skills and abilities to stay relevant. While going back to school is an option
for some, it’s out of reach for many. Who has the ability to put their life on hold
for years as they wade through classes that may or may not end up covering the
situations they actually need to deal with? Ultralearning, because it is directed by
learners themselves, can fit into a wider variety of schedules and situations,
targeting exactly what you need to learn without the waste.
Ultimately, it doesn’t matter if ultralearning is a suitable replacement for
Ultimately, it doesn’t matter if ultralearning is a suitable replacement for
higher education. In many professions, having a degree isn’t just nice, it’s
legally required. Doctors, lawyers, and engineers all require formal credentials to
even start doing the job. However, those same professionals don’t stop learning
when they leave school, and so the ability to teach oneself new subjects and
skills remains essential.
Technology: New Frontiers in Learning
Technology exaggerates both the vices and the virtues of humanity. Our vices
are made worse because now they are downloadable, portable, and socially
transmissible. The ability to distract or delude yourself has never been greater,
and as a result we are facing crises of both privacy and politics. Though those
dangers are real, there is also opportunity created in their wake. For those who
know how to use technology wisely, it is the easiest time in history to teach
yourself something new. An amount of information vaster than was held by the
Library of Alexandria is freely accessible to anyone with a device and an
internet connection. Top universities such as Harvard, MIT, and Yale are
publishing their best courses for free online. Forums and discussion platforms
mean that you can learn in groups without ever leaving your home.
Added to these new advantages is software that accelerates the act of learning
itself. Consider learning a new language, such as Chinese. A half century ago,
learners needed to consult cumbersome paper dictionaries, which made learning
to read a nightmare. Today’s learner has spaced-repetition systems to memorize
vocabulary, document readers that translate with the tap of a button, voluminous
podcast libraries offering endless opportunities for practice, and translation apps
that smooth the transition to immersion. This rapid change in technology means
that many of the best ways of learning old subjects have yet to be invented or
rigorously applied. The space of learning possibilities is immense, just waiting
for ambitious autodidacts to come up with new ways to exploit it.
Ultralearning does not require new technology, though. As I will discuss in
the chapters to come, the practice has a long history, and many of the most
famous minds could be described as having applied some version of it. However,
technology offers an incredible opportunity for innovation. There are still many
ways to learn things that we have yet to fully explore. Perhaps certain learning
tasks could be made far easier or even obsolete, with the right technical
innovation. Aggressive and efficiency-minded ultralearners will be the first to
innovation. Aggressive and efficiency-minded ultralearners will be the first to
master them.
Accelerate, Transition, and Rescue Your Career with
Ultralearning
The trends toward skill polarization in the economy, skyrocketing tuition, and
new technology are all global. But what does ultralearning actually look like for
an individual? I believe there are three main cases in which this strategy for
quickly acquiring hard skills can apply: accelerating the career you have,
transitioning to a new career, and cultivating a hidden advantage in a
competitive world.
To see how ultralearning can accelerate the career you already have, consider
Colby Durant. After graduating from college, she started work at a web
development firm but wanted to make faster progress. She took on an
ultralearning project to learn copywriting. After taking the initiative and showing
her boss what she could do, she was able to get a promotion. By choosing a
valuable skill and focusing on quickly developing proficiency, you can
accelerate your normal career progression.
Learning is often the major obstacle to transitioning to the career you want to
have. Vishal Maini, for instance, was comfortable in his marketing role in the
tech world. But he dreamed of being more closely involved with artificial
intelligence research. Unfortunately, that was a deep technical skill set that he
hadn’t acquired. Through a careful six-month ultralearning project, however, he
was able to develop strong enough skills that he could switch fields and get a job
working in the field he wanted.
Finally, an ultralearning project can augment the other skills and assets you’ve
cultivated in your work. Diana Fehsenfeld worked as a librarian for years in her
native New Zealand. Facing government cutbacks and rapid technologization of
her field, she was worried that her professional experience might not be enough
to keep up. As a result, she undertook two ultralearning projects, one to learn
statistics and the programming language R and another on data visualization.
Those skills were in demand in her industry, and adding them to her background
as a librarian gave her the tools to go from bleak prospects to being
indispensable.
Beyond Business: The Call to Ultralearning
Ultralearning is a potent skill for dealing with a changing world. The ability to
learn hard things quickly is going to become increasingly valuable, and thus it is
worth developing to whatever extent you can, even if it requires some
investment first.
Professional success, however, was rarely the thing that motivated the
ultralearners I met—including those who ended up making the most money from
their new skills. Instead it was a compelling vision of what they wanted to do, a
deep curiosity, or even the challenge itself that drove them forward. Eric Barone
didn’t pursue his passion in solitude for five years to become a millionaire but
because he wanted the satisfaction of creating something that perfectly matched
his vision. Roger Craig didn’t want to go on Jeopardy! to win prize money but to
push himself to compete on the show he had loved since he was a child. Benny
Lewis didn’t learn languages to become a technical translator, or later a popular
blogger, but because he loved traveling and interacting with the people he met
along the way. The best ultralearners are those who blend the practical reasons
for learning a skill with an inspiration that comes from something that excites
them.
There’s an added benefit to ultralearning that transcends even the skills one
learns with it. Doing hard things, particularly things that involve learning
something new, stretches your self-conception. It gives you confidence that you
might be able to do things that you couldn’t do before. My feeling after my MIT
Challenge wasn’t just a deepened interest in math and computer science but an
expansion in possibility: If I could do this, what else could I do that I was
hesitant to try before? Learning, at its core, is a broadening of horizons, of
seeing things that were previously invisible and of recognizing capabilities
within yourself that you didn’t know existed. I see no higher justification for
pursuing the intense and devoted efforts of the ultralearners I’ve described than
this expansion of what is possible. What could you learn if you took the right
approach to make it successful? Who could you become?
What About Talent? The Terence Tao Problem
Terence Tao is smart. By age two, he had taught himself to read. At age seven,
he was taking high school math classes. By seventeen, he had finished his
master’s thesis. It was titled “Convolution Operators Generated by RightMonogenic and Harmonic Kernels.” After that, he got a PhD from Princeton,
won the coveted Fields Medal (called by some the “Nobel Prize for
mathematics”), and is considered to be one of the best mathematical minds alive
today. Though many mathematicians are extreme specialists—rare orchids
adapted to thrive only on a particular branch of the mathematical tree—Tao is
phenomenally diverse. He regularly collaborates with mathematicians and makes
important contributions to distant fields. This virtuosity caused one colleague to
liken his ability to “a leading English-language novelist suddenly producing the
definitive Russian novel.”
4
What’s more, there doesn’t seem to be an obvious explanation for his feats.
He was precocious, certainly, but his success in mathematics didn’t come from
aggressively overbearing parents pushing him to study. His childhood was filled
playing with his two younger brothers, inventing games with the family’s
Scrabble board and mah-jongg tiles, and drawing imaginary maps of fantasy
terrain. Normal kid stuff. Nor does he seem to have a particularly innovative
studying method. As noted in his profile in the New York Times, he coasted on
his intelligence so far that, upon reaching his PhD, he fell back “on his usual
test-prep strategy: last-minute cramming.” Although that approach faltered once
he reached the pinnacle of his field, the fact that he breezed through classes for
so long points to a powerful mind rather than some unique strategy. Genius is a
word thrown around too casually, but in Tao’s case the label certainly sticks.
Terence Tao and other naturally gifted learners present a major challenge for
the universality of ultralearning. If people like Tao can accomplish so much
without aggressive or inventive studying methods, why should we bother
investigating the habits and methods of other impressive learners? Even if the
feats of Lewis, Barone, or Craig don’t reach the level of Tao’s brilliance,
perhaps their accomplishments also are due to some hidden intellectual ability
that normal people lack. If this were so, ultralearning might be something
interesting to examine but not something you could actually replicate.
Putting Talent Aside
What role does natural talent play? How can we examine what causes someone’s
success when the shadow of intelligence and innate gifts looms over us? What
do stories like Tao’s mean for mere mortals who just want to improve their
capacity to learn?
capacity to learn?
The psychologist K. Anders Ericsson argues that particular types of practice
can change most attributes necessary for becoming an expert-level performer
with the exception of the innate traits of height and body size. Other researchers
are less optimistic about the malleability of our natures. Many argue that a
substantial proportion, perhaps most, of our intelligence is genetically derived. If
intelligence comes mostly from genes, why not use that to explain ultralearning
instead of ultralearners’ use of a more effective method or strategy? Tao’s
success in mathematics doesn’t seem to be owed to something easily replicable
by normal human beings, so why assume that any of the ultralearners are any
different?
I take a middle position between those two extremes. I think that natural
talents exist and that they undoubtedly influence the results we see (especially at
extreme levels, as in the case of Tao). I also believe that strategy and method
matter, too. Throughout this book, I will cover science showing how making
changes to how you learn can impact your effectiveness. Each of the principles
is something that, if applied appropriately, will make you a better learner
regardless of whether your starting point is dull or brilliant.
My approach in telling stories for this book, therefore, will not be to try to
determine what the sole cause is of someone’s intellectual success. Not only is
this impossible, but it isn’t particularly useful. Instead, I’m going to use stories
and anecdotes to illustrate and isolate what are the most practical and useful
things you can do to improve how you learn. The ultralearners I mention should
serve as exemplars you can use to see how a principle applies in practice, not a
guarantee that you can achieve an identical result with identical effort.
Finding Time for Ultralearning
Another doubt that may have formed in your mind when reading so far is asking
how you’ll find time to do these intensive learning projects. You may worry that
this advice won’t apply to you because you already have work, school, or family
commitments that prevent you from throwing yourself into learning full-time.
In practice, however, this usually isn’t a problem. There are three main ways
you can apply the ideas of ultralearning, even if you have to manage other
commitments and challenges in your life: new part-time projects, learning
sabbaticals, and reimagining existing learning efforts.
The first way is by pursuing ultralearning part-time. The most dramatic
examples of learning success tend to be those where the ultralearner put
impressive amounts of time into the project. Spending fifty hours a week on a
project will accomplish more than spending five hours a week on it, even if the
efficiency is the same, and thus the most captivating stories usually involve
heroic schedules. Though this makes for good storytelling, it’s actually
unnecessary when it comes to pursuing your own ultralearning projects. The
core of the ultralearning strategy is intensity and a willingness to prioritize
effectiveness. Whether this happens on a full-time schedule or just a couple
hours per week is completely up to you. As I’ll discuss in chapter 10, a spreadout schedule may even be more efficient in terms of long-term memory.
Whenever you read about an intensive schedule in this book, feel free to adapt it
to your own situation, taking a more leisurely pace while employing the same
ruthlessly efficient tactics.
The second way is by pursuing ultralearning during gaps in work and school.
Many of the people I interviewed did their projects during temporary
unemployment, career transitions, semesters off, or sabbaticals. Although these
aren’t as reliable to plan for, a burst of learning may be perfect for you if you
know you’re about to have this kind of time off. That was one of my motivations
for pursuing my MIT Challenge when I did: I had just graduated, so extending
my existing student life another year was easier than pushing it out for four. If I
had to do the same project today, I might have done it over a longer period of
time, over some evenings and weekends, since my work is less flexible today
than it was in that moment of transition from school to working life.
The third way is to integrate ultralearning principles into the time and energy
you already devote to learning. Think about the last business book you read or
the time you tried to pick up Spanish, pottery, or programming. What about that
new software you needed to learn for work? Those professional development
hours you need to log to maintain your certification? Ultralearning doesn’t have
to be an additional activity; it can inform the time you already spend learning.
How can you align the learning and studies you already need to do with the
principles for maximizing effectiveness?
As in the section on talent, don’t let the extreme examples dissuade you from
applying the same principles. Everything I will share with you can be
customized or integrated into what already exists. What matters is the intensity,
initiative, and commitment to effective learning, not the particulars of your
timetable.
The Value of Ultralearning
The ability to acquire hard skills effectively and efficiently is immensely
valuable. Not only that, but the current trends in economics, education, and
technology are going to exacerbate the difference between those with this skill
and those without it. In this discussion, however, I’ve ignored perhaps the most
important question: Ultralearning may be valuable, but is it learnable? Is
ultralearning just a description of people with unusual personalities, or does it
represent something that someone who wasn’t an ultralearner before could
actually become?
Chapter III
How to Become an Ultralearner
“I’d love to be a guinea pig.” It was an email from Tristan de Montebello. I had
first met the charming, half-French, half-American musician and entrepreneur
seven years earlier, at almost exactly the same time as my fateful encounter with
Benny Lewis. With tousled blond hair and a close-cropped beard, he looked like
he belonged on a surfboard on some stretch of California coastline. De
Montebello was the kind of guy you liked immediately: confident, yet down to
earth, with only the vaguest hints of a French accent in his otherwise perfect
English. Over the years, we had kept in touch: me with my strange learning
experiments; him hopping around the world, going from working with a Parisian
startup that made bespoke cashmere sweaters to guitarist, vagabond, and
eventually web consultant in Los Angeles, much closer to the beaches that suited
him so well. Now he had heard I was writing a book about learning, and he was
interested.
The context of his email was that although I had met and documented dozens
of people accomplishing strange and intriguing learning feats, the meetings had
been largely after the fact. They were people I had met or heard about after their
successes, not before; observations of successes, not experiments that generated
them. As a result, it was hard to tell exactly how accessible this ultralearning
thing was. If you filter through enough pebbles, you’re sure to find a few flecks
of gold. Was I doing the same thing, scouring for unusual learning projects? Sift
through enough people, and you’re bound to find some that seem incredible. But
if ultralearning had the potential that I imagined it did, it would be nice to find
someone before he or she tried a project and watch the results. To test this, I put
together a small group of about a dozen people (mostly readers of my blog) who
together a small group of about a dozen people (mostly readers of my blog) who
were interested in giving this ultralearning thing a shot. Among them was de
Montebello.
Becoming an Ultralearner
“Maybe piano?” de Montebello suggested. Although he was interested in the
concept of ultralearning, he had no idea which skill he’d like to learn. He had
played guitar and been the lead singer for a band. With his musical background,
learning to play piano seemed like a relatively safe choice. He had even made a
course teaching guitar lessons online, so learning another musical instrument
could potentially expand his business. Selfishly, I encouraged him to try learning
something farther outside his comfort zone. A musician picking up another
instrument didn’t seem like the ideal case to study for seeing whether
ultralearning could be applied broadly. We threw more ideas around. A week or
two later, he decided on public speaking. His background as a musician had
given him experience being onstage, but otherwise he had little experience
giving speeches. Public speaking is a useful skill, too, he argued, so it would be
worth getting better at even if nothing noteworthy came from the effort.
De Montebello had a private motivation for wanting to become good at public
speaking. He had given only a handful of speeches in his life, and most of those
had been in college. He related to me one example, from when he had gone to
give a talk to a dozen people at a web design firm in Paris: “I cringe every time I
think back to that.” He explained, “I could just tell I wasn’t connecting. There
were many pieces where I was boring them. There were jokes where I would
laugh, because I thought it was funny, but nobody else would.” Being a
musician, he was surprised “how little of it translated” to public speaking. Still,
it was something in which he saw potential value, if he could get good at it.
“Public speaking is a metaskill,” he feels. It’s the kind of skill that assists with
other skills: “confidence, storytelling, writing, creativity, interviewing skills,
selling skills. It touches on so many different things.” With that in mind, he set
to work.
First Steps of a Fledgling Ultralearner
De Montebello had picked his topic, but he wasn’t sure exactly how he should
De Montebello had picked his topic, but he wasn’t sure exactly how he should
learn it. He decided to attend a meeting of Toastmasters International, the
organization for learning public speaking. At that point, his story had two doses
of luck. The first was that in attendance at the very first meeting for his public
speaking project was Michael Gendler. Gendler was a longtime Toastmaster, and
de Montebello’s combination of charm and obsessive intensity to become good
at public speaking convinced him to help coach de Montebello through his
project. The second dose of luck was something that de Montebello didn’t fully
appreciate at the time: he had showed up just ten days before the deadline to be
eligible to compete in the World Championship of Public Speaking.
The World Championship of Public Speaking is a competition put on annually
by Toastmasters in which members compete, elimination style, starting in
individual clubs and going on to larger and larger units of the organization, until
a select few make it to the final stage. De Montebello had little more than a week
to prepare. Still, the competition provided a potential structure for his
ultralearning project, so he went for it, cranking out the six mandatory qualifying
speeches in the coming week, finishing the last in the nick of time.
De Montebello practiced obsessively, sometimes speaking twice in one day.
He recorded a video of every speech and analyzed it obsessively for flaws. He
asked for feedback every time he gave a speech, and he got plenty of it. His
coach, Gendler, pushed him far outside of his comfort zone. Once, when faced
with the choice between polishing an existing speech and creating a brand-new
one from scratch, de Montebello asked what he should do. Gendler’s response
was to do whichever was scariest for him.
His relentless drive pushed de Montebello into unusual places. He took
improv classes to work on his spontaneous delivery. There he learned to trust
whatever was in his head and deliver it without hesitation. That kept him from
stammering over his words or fearing freezing up onstage. He talked to a friend
who works as a Hollywood director to give feedback on his delivery. The
director taught de Montebello to give his speech dozens of times in different
styles—angry, monotone, screaming, even as a rap—then go back and see what
was different from his normal voice. According to de Montebello, that helped
break him of the “uncanny valley” that happened when his normal speaking
delivery felt slightly unnatural.
Another friend with a background in theater gave him tips on stage presence.
He took de Montebello through his speech and showed how each word and
sentence indicated movement that could be translated to where he moved on the
stage. Instead of standing constricted under the spotlight, de Montebello could
stage. Instead of standing constricted under the spotlight, de Montebello could
now move gracefully and use his body to communicate his message on top of his
words. He even gave his speech at a middle school, knowing that seventh
graders would give the most ruthless feedback of all. After bombing terribly
outside the comfort of Toastmasters, he learned to talk to his audience before
going onstage: learn their language and emotions and connect with them. That
way, applying all he had learned so far, he could change his speech on the fly, so
it would be sure to connect with a new audience. Above all, Gendler pushed him
relentlessly. “Make me care,” Gendler told him after listening to one of de
Montebello’s speeches. “I understand why this is important to you, but the
audience doesn’t care about you. You have to make me care.” Diverse advice
and voluminous practice would soak those lessons in deeply, allowing de
Montebello to quickly surpass his early awkwardness on the stage.
After a month, de Montebello won his area competition, beating out a
competitor with two decades of experience in Toastmasters. He won his district
and division competitions, too. Finally, less than seven months after he first tried
his hand at public speaking, he was going to compete in the World
Championships. “There are about thirty thousand people who compete every
year,” he noted, adding “I’m pretty confident I’m the fastest competitor in
history to make it this far, since if I had started ten days later, I couldn’t have
competed.” He made it into the top ten.
From Semifinalist to Career Change
“I knew this project was going to be big for me when I started it,” de Montebello
told me months after his top ten placement in the international competition. “But
it was literally life changing. I didn’t expect it to actually change my life.”
Reaching the final competition in the World Championship had been quite a
journey, but it was only afterward that he began to realize how much he had
learned. “I was learning for this very narrow world of public speaking. It was
only after that I realized the depth of all these skills I had worked so much on:
storytelling, confidence, communicating.”
Friends who heard of de Montebello’s success started asking if he could help
them work on their own speeches. He and Gendler saw an opportunity to help
others improve their public speaking skills. The demand was intense. Authors
who command five-figure speaking fees started to approach the duo to see if
they could be taught to improve their public speaking the ultralearning way.
Soon they had landed their first client, to the tune of twenty thousand dollars.
Soon they had landed their first client, to the tune of twenty thousand dollars.
Gendler and de Montebello weren’t mercenary; they wanted to focus only on
speakers whose message they really believed in. But the fact that they had
attracted such high-status clients certainly helped persuade them to make the
switch into coaching public speaking full-time. Gendler and de Montebello even
decided to name their consultancy UltraSpeaking, as a nod to the strategy that
made it all possible.
De Montebello’s story ended up being much more dramatic than either of us
had initially expected. His initial hope had been that he could practice intensely
for a few months, deliver a great speech somewhere, and have it recorded—a
nice memento and a new skill—but not that he would reach the status of an
international competitor and eventually experience a complete career change. Of
the other dozen or so people I spent some time coaching into ultralearning, none
offered so dramatic an example. Some dropped out. Life got in the way (or
perhaps revealed that they weren’t actually as committed as they had initially
appeared to be). Others had respectable successes, making significant
improvements in learning medicine, statistics, comic book drawing, military
history, and yoga, even if they didn’t reach de Montebello’s degree of success.
What differentiated de Montebello wasn’t that he thought he could go from
near-zero experience to the finalist for the World Championship in six months.
Rather, it was his obsessive work ethic. His goal wasn’t to reach some
predetermined extreme but to see how far he could go. Sometimes you’ll get
lucky and embark on a path that will take you quite far. But even the failure
mode of ultralearning is usually that you will learn a skill fairly well. Even those
who didn’t have such dramatic results among the small group I spent time
coaching, those who stuck with their project still ended up learning a new skill
they cared about. You may not compete in world championships or completely
switch careers, but as long as you stick with the process, you’re bound to learn
something new. What de Montebello’s example encapsulated for me was not
only that you can become an ultralearner but that such successes are far from
being the inevitable consequences of having a particular kind of genius or talent.
Had de Montebello focused on piano instead, his experience with giving
speeches would probably have remained that one awkward example from his
days in Paris.
Principles of Becoming an Ultralearner
De Montebello’s story illustrates that it’s possible to decide to become an
ultralearner. But ultralearning isn’t a cookie-cutter method. Every project is
unique, and so are the methods needed to master it. The uniqueness of
ultralearning projects is one of the elements that ties them all together. If
ultralearning could be bottled or standardized, it would simply be an intense
form of structured education. What makes ultralearning interesting is also what
makes it hard to boil down into step-by-step formulas.
This is a difficult challenge, but I’m going to try to sidestep it by focusing on
principles first. Principles allow you to solve problems, even those you may have
never encountered before, in a way that a recipe or mechanical procedure cannot.
If you really understand the principles of physics, for instance, you can solve a
new problem simply by working backward. Principles make sense of the world,
and even if they don’t always articulate exactly how you should solve a
particular challenge, they can provide immense guidance. Ultralearning, in my
view, works best when you see it through a simple set of principles, rather than
trying to copy and paste exact steps or protocols.
The principles of ultralearning are going to be the focus of the second part of
this book. In each chapter, I’ll introduce a new principle, plus some evidence to
back it up both from ultralearning examples and from scientific research.
Finally, I’ll share possible ways that the principle can manifest itself as specific
tactics. These tactics are only a small sample. But they should provide a starting
point for you to think creatively about your own ultralearning challenges.
There are nine universal principles that underlie the ultralearning projects
described so far. Each embodies a particular aspect of successful learning, and I
describe how ultralearners maximize the effectiveness of the principle through
the choices they make in their projects. They are:
1. METALEARNING: FIRST DRAW A MAP. Start by learning how to learn the
subject or skill you want to tackle. Discover how to do good research and
how to draw on your past competencies to learn new skills more easily.
2. FOCUS: SHARPEN YOUR KNIFE. Cultivate the ability to concentrate. Carve
out chunks of time when you can focus on learning, and make it easy to just
do it.
3. DIRECTNESS: GO STRAIGHT AHEAD. Learn by doing the thing you want
to become good at. Don’t trade it off for other tasks, just because those are
more convenient or comfortable.
4. DRILL: ATTACK YOUR WEAKEST POINT. Be ruthless in improving your
weakest points. Break down complex skills into small parts; then master
those parts and build them back together again.
5. RETRIEVAL: TEST TO LEARN. Testing isn’t simply a way of assessing
knowledge but a way of creating it. Test yourself before you feel confident,
and push yourself to actively recall information rather than passively review
it.
6. FEEDBACK: DON’T DODGE THE PUNCHES. Feedback is harsh and
uncomfortable. Know how to use it without letting your ego get in the way.
Extract the signal from the noise, so you know what to pay attention to and
what to ignore.
7. RETENTION: DON’T FILL A LEAKY BUCKET. Understand what you forget
and why. Learn to remember things not just for now but forever.
8. INTUITION: DIG DEEP BEFORE BUILDING UP. Develop your intuition
through play and exploration of concepts and skills. Understand how
understanding works, and don’t recourse to cheap tricks of memorization to
avoid deeply knowing things.
9. EXPERIMENTATION: EXPLORE OUTSIDE YOUR COMFORT ZONE. All of
these principles are only starting points. True mastery comes not just from
following the path trodden by others but from exploring possibilities they
haven’t yet imagined.
I organized these nine principles based on my observations of ultralearning
projects as well as my own personal experience, referencing, where I could, the
vast cognitive science literature. I started with the ultralearners themselves. If
one person did something in a certain way, that might be an interesting example,
but it might also be an idiosyncrasy of that person. If several people or, better
yet, every ultralearner I encountered, did a certain thing in a certain way, it was
much stronger evidence that I had stumbled upon a general principle. I then
checked those principles against the scientific literature. Are there mechanisms
and findings from cognitive science to support the tactics I saw? Better yet, have
there been controlled experiments comparing one approach to learning with
another? The scientific research supports many of the learning strategies
employed by the ultralearners I witnessed. This suggests that ultralearners, with
their ruthless focus on efficiency and effectiveness, may have landed on some
universal principles in the art of learning.
Beyond principles and tactics is a broader ultralearning ethos. It’s one of
taking responsibility for your own learning: deciding what you want to learn,
how you want to learn it, and crafting your own plan to learn what you need to.
You’re the one in charge, and you’re the one who’s ultimately responsible for
the results you generate. If you approach ultralearning in that spirit, you should
take these principles as flexible guidelines, not as rigid rules. Learning well isn’t
just about following a set of prescriptions. You need to try things out for
yourself, think hard about the nature of the learning challenges you face, and test
solutions to overcome them. With that in mind, let’s turn to the first ultralearning
principle: metalearning.
Chapter IV
Principle 1
Metalearning
First Draw a Map
If I have seen further it is by standing on the shoulders of giants.
—Isaac Newton
Dan Everett stands in front of a packed auditorium. A stocky man in his early
sixties, he speaks slowly and confidently, his smiling face framed by thinning
blond hair and a beard. Next to him is a table filled with assorted objects: sticks,
stones, leaves, containers, fruit, a pitcher of water. He signals that the
demonstration is about to begin.
Entering from a door on the right, a heavyset middle-aged woman with dark
brown hair and olive skin approaches the stage. Everett goes up to her and says
something in a language she doesn’t understand. She looks around, clearly
confused, and then replies hesitantly, “Kuti paoka djalou.”
1 He tries to repeat
what she has just said. There’s some stumbling at first, but after one or two more
tries, she seems satisfied with his repetition of her reply. He goes to the
blackboard and writes, “Kuti paoka djalou ➱ ‘Greeting (?).’” Next he picks up a
small stick and points to it. She guesses correctly that he wants to know the
name and replies, “ŋkindo.” Once again, Everett goes to the blackboard and
writes, “ŋkindo ➱ stick.” Next he tries two sticks and gets the same response,
“ŋkindo.” He then drops the stick, to which the woman says, “ŋkindo paula.”
The demonstration proceeds, with Everett picking up objects, performing
actions, listening to the woman’s responses, and recording the results on the
blackboard. Soon he’s moved past simple naming tasks and starts asking for
more complicated sentences: “She drinks the water,” “You eat the banana,” and
“Put the rock in the container.” With each new elicitation, he experiments,
building new sentences and testing her reaction to see if he is correct. Within
half an hour, there are more than two blackboards full of nouns, verbs, pronouns,
and phonetic annotations.
Learning dozens of words and phrases in a new language is a good start for
the first thirty minutes spent with any language. What makes this feat
particularly impressive is that Everett isn’t allowed to speak any language he
might have in common with the speaker.
2 He can only try to encourage her to
say words and phrases and repeat them to try to figure out the language’s
grammar, pronunciation, and vocabulary. He doesn’t even know what language
is being spoken.*
How can Everett start speaking a new language from scratch, without teachers
or translations or even knowing what language he’s learning, in half an hour,
when most of us struggle to do the same after years of high school Spanish
classes? What enables Everett to pick up vocabulary, decode grammar, and
pronunciation so much faster than you or I, even with all those additional
constraints? Is he a linguistic genius, or is there something else going on?
The answer is our first principle of ultralearning: metalearning.
What Is Metalearning?
The prefix meta comes from the Greek term μετά, meaning “beyond.” It
typically signifies when something is “about” itself or deals with a higher layer
of abstraction. In this case metalearning means learning about learning. Here’s
an example: If you’re learning Chinese characters, you will learn that ⽕ means
“fire.” That’s regular learning. You may also learn that Chinese characters are
often organized by something called radicals, which indicate what kind of thing
the character describes. The character 灶, for example which means “stove,” has
a ⽕ on the left-hand side to indicate that it has some relationship to fire.
Learning this property of Chinese characters is metalearning—not learning about
the object of your inquiry itself, in this case words and phrases, but learning
about how knowledge is structured and acquired within this subject; in other
words, learning how to learn it.
In Everett’s case, we can see glimpses of the enormous wealth of
metalearning that lies just beneath the surface. “Well, what are some of the
things we noticed about this?” Everett asks the audience after his brief
demonstration has concluded, “It seems to be SVO, a subject-verb-object
language, that’s not terribly shocking.” He continues, “There doesn’t seem to be
any plural marking on the nouns, unless it’s tones and I missed it. . . . There’s
clearly pitch going on here; whether it’s tone remains to be analyzed.” From this
jargon we can see that when Everett evokes a word or phrase from his
interlocutor, he isn’t just parroting back the sounds; he’s drawing a map with
theories and hypotheses about how the language works grounded on years of
experience learning languages.
In addition to his enormous wealth of knowledge as a linguist, Everett has
another trick that gives him an enormous advantage. The demonstration he has
presented is not his own invention. Called a “monolingual fieldwork”
demonstration, this method was first developed by Everett’s teacher Kenneth
Pike as a means of learning indigenous languages. The method lays out a
sequence of objects and actions that the practitioner can use to start piecing
together the language. This method even received some Hollywood exposure
after Louise Banks, a fictional linguist, used it to decode an alien language in the
2016 science fiction movie Arrival.
These two pieces in Everett’s linguistic arsenal—a richly detailed map of how
languages work and a method that provides a path to fluency—have allowed
Everett to accomplish a lot more than just learning some simple sentences. Over
the last thirty years he has become one of only a handful of outsiders to become
fluent in Pirahã, one of the most unusual and difficult languages on the planet,
spoken only by a remote tribe in the Amazon jungle.
3
The Power of Your Metalearning Map
Everett’s case beautifully illustrates the power of using metalearning to learn
new things faster and more effectively. Being able to see how a subject works,
what kinds of skills and information must be mastered, and what methods are
available to do so more effectively is at the heart of success of all ultralearning
projects. Metalearning thus forms the map, showing you how to get to your
projects. Metalearning thus forms the map, showing you how to get to your
destination without getting lost.
To see why metalearning is so important, consider one study on the helpful
effects of already knowing a second language when learning a third.
4 The study
took place in Texas, where monolingual English speakers and bilingual
Spanish/English speakers were enrolled in a French class. Follow-up on
subsequent tests showed that the bilingual speakers outperformed the
monolingual students when learning a new language. On its own, this isn’t
terribly surprising. French and Spanish are both Romance languages, so there are
shared features of grammar and vocabulary that aren’t present in English that
could conceivably provide an advantage. More interesting, however, is that even
among the Spanish/English bilinguals, those who also took Spanish classes
ended up doing better when they later needed to learn French. The reason seems
to be that taking classes assists with helping form what the study authors call
metalinguistic awareness in a way that simply knowing a language informally
does not. The difference between the two types of bilingual speakers mostly
came down to metalearning: one group had content knowledge of the language,
but the group that took classes also had knowledge about how information in a
language is structured.*
Nor is this idea about metalearning restricted to languages. Linguistic
examples are often easier to study because there’s a cleaner separation of
metalearning and regular learning. This is because the contents of unrelated
languages, such as vocabulary and grammar, are often quite different, even if the
metalearning structure is the same. Learning French vocabulary won’t help you
much with learning Chinese vocabulary, but understanding how vocabulary
acquisition works in French will likely also help with learning Chinese. By the
time my friend and I had reached the last country in our year of learning
languages, the process of immersing ourselves and learning a new language from
scratch was practically a routine. The words and grammar of Korean may have
been completely new, but the process of learning was already well trodden.
Metalearning exists in all subjects, but it can often be harder to examine
independently from regular learning.
How to Draw Your Map
Now that you have some idea what metalearning is and its importance to
learning faster, how can you apply this to get an edge in your own learning
learning faster, how can you apply this to get an edge in your own learning
efforts? There are two main ways: over the short term and over the long term.
Over the short term, you can do research to focus on improving your
metalearning before and during a learning project. Ultralearning, owing to its
intensity and self-directed nature, has the opportunity for a lot higher variance
than normal schooling efforts do. A good ultralearning project, with excellent
materials and an awareness of what needs to be learned, has the potential to be
completed faster than formal schooling. Language learning through intensive
immersion can beat lengthy classes. Aggressively paced coding bootcamps can
get participants up to a level where they can compete for jobs much faster than
those with a normal undergraduate degree. This is because you can tailor your
project to your exact needs and abilities, avoiding the one-size-fits-all approach
taken in school. However, there’s also a danger of choosing unwisely and ending
up much worse off. Metalearning research avoids this problem and helps you
seek out points where you might even be able to get a significant advantage over
the status quo.
Over the long term, the more ultralearning projects you do, the larger your set
of general metalearning skills will be. You’ll know what your capacity is for
learning, how you can best schedule your time and manage your motivation, and
you’ll have well-tested strategies for dealing with common problems. As you
learn more things, you’ll acquire more and more confidence, which will allow
you to enjoy the process of learning more with less frustration.
In this chapter, I’m going to devote most of the next section to short-term
research strategies, since they will probably benefit you the most. However, this
emphasis shouldn’t undermine the importance of the long-term effects of
metalearning. Ultralearning is a skill, just like riding a bicycle. The more
practice you get with it, the more skills and knowledge you’ll pick up for how to
do it well. This long-term advantage likely outweighs the short-term benefits and
is what’s easiest to mistake for intelligence or talent when seen in others. My
hope is that as you get more practice in ultralearning, you’ll start to
automatically apply many of those skills to learn faster and more effectively.
Determining Why, What, and How
I find it useful to break down metalearning research that you do for a specific
project into three questions: “Why?,” “What?,” and “How?” “Why?” refers to
understanding your motivation to learn. If you know exactly why you want to
understanding your motivation to learn. If you know exactly why you want to
learn a skill or subject, you can save a lot of time by focusing your project on
exactly what matters most to you. “What?” refers to the knowledge and abilities
you’ll need to acquire in order to be successful. Breaking things down into
concepts, facts, and procedures can enable you to map out what obstacles you’ll
face and how best to overcome them. “How?” refers to the resources,
environment, and methods you’ll use when learning. Making careful choices
here can make a big difference in your overall effectiveness.
With these three questions in mind, let’s take a look at each of them and how
you can draw your map.
Answering “Why?”
The first question to try to answer is why are you learning and what that implies
for how you should approach the project. Practically speaking, the projects you
take on are going to have one of two broad motivations: instrumental and
intrinsic.
Instrumental learning projects are those you’re learning with the purpose of
achieving a different, nonlearning result. Consider the previously mentioned
case of Diana Fehsenfeld, who, after a few decades as a librarian, found that her
job was becoming obsolete. Computerized file systems and budget cuts meant
she would need to learn new skills to stay relevant. She did some research and
decided that the best way to do this would be to get a firmer grasp on statistics
and data visualization. In this case, she wasn’t learning because of her deep love
of statistics and data visualization but because she believed that doing so would
benefit her career.
Intrinsic projects are those that you’re pursuing for their own sake. If you’ve
always wanted to speak French, even though you’re not sure how you’ll use it
yet, that’s an intrinsic project. Intrinsic doesn’t mean useless. Learning French
might have benefits later when you decide to travel or need to work with a client
from France at your job. The difference is that you’re learning the subject for its
own sake, not as a means to some other outcome.
If you’re pursuing a project for mostly instrumental reasons, it’s often a good
idea to do an additional step of research: determining whether learning the skill
or topic in question will actually help you achieve your goal. I’ve often heard
stories of people unhappy with their career progress who decide that attending
graduate school is the answer. If only they had an MBA or an MA, employers
would take them more seriously, they think, and they’d have the career they
desire. So they go off to school for two years, rack up tens of thousands of
dollars in student debt, and discover that their newly minted credentials don’t
actually get them much better job opportunities than before. The fix here is to do
your research first. Determine if learning a topic is likely to have the effect you
want it to before you get started.
5
Tactic: The Expert Interview Method
The main way you can do research of this kind is to talk to people who have
already achieved what you want to achieve. Let’s say you want to become a
successful architect and think that mastering design skills might be the best step
to take. Before you get started, it would be a good idea to talk to some successful
architects to get a sense of whether they think your project will actually help
with your intended goal. Though this method can be used for many parts of the
research process, I’ve found it particularly valuable for vetting instrumental
projects. If someone who has already accomplished the goal you want to achieve
doesn’t think your learning project will help reach it or thinks it’s less important
than mastering some other skill, that’s a good sign that your motivation and the
project are misaligned.
Finding such people isn’t as hard as it sounds. If your goal is career related,
look for people who have the career you want and send them an email. You can
find them at your current workplace, conferences, or seminars, or even on social
networking websites such as Twitter or LinkedIn. If your goal is related to
something else, you can search online in forums dedicated to the subject you
want to learn. If you want to learn programming, with the goal of building your
own apps, for example, you can find online forums dedicated to programming or
app development. Then you just need to look for frequent posters who seem to
have the knowledge you’re looking for and email them.
Reaching out and setting up a meeting with an expert isn’t hard, either, but it’s
a step many people shy away from. Many people, particularly the introverts
among us, recoil at the idea of reaching out to a stranger to ask for advice. They
worry that they’ll be rejected, ignored, or even yelled at for presuming to take up
a person’s time. The truth is, however, that this rarely happens. Most experts are
more than willing to offer advice and are flattered by the thought that someone
wants to learn from their experience. The key is to write a simple, to-the-point
wants to learn from their experience. The key is to write a simple, to-the-point
email, explaining why you’re reaching out to them and asking if they could spare
fifteen minutes to answer some simple questions. Make the email concise and
nonthreatening. Don’t ask for more than fifteen minutes or for ongoing
mentorship. Though some experts will be happy to help you in those ways, it’s
not good form to ask for too much in the first email.
What if the person you want to interview doesn’t live in your city? In that
case, phone or online calls are great alternatives.* Email can work in a pinch,
too, but I’ve found that text often doesn’t translate tone well, and you often miss
sensing the way the person feels about your project. Saying it’s a “great idea”
lukewarmly versus enthusiastically can make a world of difference, yet that
nuance is missing if you communicate via text only.
Even if your project is intrinsically motivated, asking “Why?” is still very
useful. Most learning plans you might choose to emulate will be based on
curriculum designers’ ideas of what is important for you to learn. If these aren’t
perfectly lined up with your own goals, you may end up spending a lot of time
learning things that aren’t important to you or underemphasizing the things that
do matter. For these kinds of projects, it’s useful to ask yourself what you’re
trying to learn because it will help you evaluate different study plans for their fit
with your goals.
Answering “What?”
Once you’ve gotten a handle on why you’re learning, you can start looking at
how the knowledge in your subject is structured. A good way to do this is to
write down on a sheet of paper three columns with the headings “Concepts,”
“Facts,” and “Procedures.” Then brainstorm all the things you’ll need to learn. It
doesn’t matter if the list is perfectly complete or accurate at this stage. You can
always revise it later. Your goal here is to get a rough first pass. Once you start
learning, you can adjust the list if you discover that your categories aren’t quite
right.
Concepts
In the first column, write down anything that needs to be understood. Concepts
are ideas that you need to understand in flexible ways in order for them to be
useful. Math and physics, for example, are both subjects that lean heavily toward
useful. Math and physics, for example, are both subjects that lean heavily toward
concepts. Some subjects straddle the concept/fact divide, such as law, which has
legal principles that need to be understood, as well as details that need to be
memorized. In general, if something needs to be understood, not just memorized,
I put it into this column instead of the second column for facts.
Facts
In the second column, write down anything that needs to be memorized. Facts
are anything that suffices if you can remember them at all. You don’t need to
understand them too deeply, so long as you can recall them in the right
situations. Languages, for instance, are full of facts about vocabulary,
pronunciation, and, to a lesser extent, grammar. Even concept-heavy subjects
usually have some facts. If you’re learning calculus, you will need to deeply
understand how derivatives work, but it may be sufficient to memorize some
trigonometric identities.
Procedures
In the third column, write down anything that needs to be practiced. Procedures
are actions that need to be performed and may not involve much conscious
thinking at all. Learning to ride a bicycle, for instance, is almost all procedural
and involves essentially no facts or concepts. Many other skills are mostly
procedural, while others may have a procedural component yet still have facts to
memorize and concepts to understand. Learning new vocabulary in a language
requires memorizing facts, but pronunciation requires practice and therefore
belongs in this column.
Using This Analysis to Draw Your Map
Once you’ve finished your brainstorm, underline the concepts, facts, and
procedures that are going to be most challenging. This will give you a good idea
what the major learning bottlenecks are going to be and can start you searching
for methods and resources to overcome those difficulties. You might recognize
that learning medicine requires a lot of memorization, so you may invest in a
system such as spaced-repetition software. If you’re learning mathematics, you
might recognize that deep understanding of certain concepts is going to be the
tricky spot and consider spending time explaining those concepts to other people
so you really understand them yourself. Knowing what the bottlenecks will be
can help you start to think of ways of making your study time more efficient and
effective, as well as avoid tools that probably won’t be too helpful to your goal.
Often this coarse-grained analysis is enough to move on to the next phase of
research. However, with more experience, you can dig deeper. You might look
at some of the particular features of the concepts, facts, and procedures you’re
trying to learn to find methods to master them more effectively. When I started
my portrait-drawing challenge, for instance, I knew that success would depend
highly on how accurately I could size and place facial features. Most people
can’t draw realistic faces because if those attributes are off even slightly (such as
making a face too wide or the eyes too high), they will instantly look wrong to
our sophisticated ability to recognize faces. Therefore, I got the idea of doing
lots and lots of sketches and comparing them by overlaying the reference photos.
That way I could quickly diagnose what kinds of errors I was making without
having to guess. If you can’t make these kinds of predictions and come up with
these kinds of strategies just yet, don’t worry. This is the kind of long-term
benefit of metalearning that comes from having done more projects.
Answering “How?”
Now that you’ve answered two questions—why you’re learning and what you’re
learning—it’s time to answer the final question: How are you going to learn it?
I suggest following two methods to answer how you’ll learn something:
Benchmarking and the Emphasize/Exclude Method.
Benchmarking
The way to start any learning project is by finding the common ways in which
people learn the skill or subject. This can help you design a default strategy as a
starting point.
If I’m trying to learn something that is taught in school, say computer science,
neurology, or history, one thing I’ll do is look at the curricula used in schools to
teach that subject. This could be the syllabus from a single class or, as in the case
of my MIT Challenge, the course list for an entire degree. When I wanted to
of my MIT Challenge, the course list for an entire degree. When I wanted to
learn more about cognitive science, I found a list of textbooks that the University
of San Diego’s Cognitive Science doctoral program recommends for incoming
students without cognitive science backgrounds. Good resources to consider for
this approach are universities (MIT, Harvard, Yale, and Stanford are good
examples but far from the only ones). Generally course lists and syllabi are
available by looking on their websites aimed at existing students.
If I’m trying to learn a nonacademic subject or a professional skill, I’ll
probably instead do online searches for people who have previously learned that
skill or use the Expert Interview Method to focus on resources available for
mastering that subject. An hour spent searching online for almost any skill
should turn up courses, articles, and recommendations for how to learn it.
Investing the time here can have incredible benefits because the quality of the
materials you use can create orders-of-magnitude differences in your
effectiveness. Even if you’re eager to start learning right away, investing a few
hours now can save you dozens or hundreds later on.
The Emphasize/Exclude Method
Once you’ve found a default curriculum, you can consider making modifications
to it. I find this easier to do with skills that have obvious success criteria (say
drawing, languages, or music) and for which you can generally make a guess at
the relative importance to the subject topics prior to studying them. For
conceptual subjects or topics where you may not even understand the meaning of
the terms in the syllabus, it’s probably better to stick closer to your benchmark
until you learn a bit more.
The Emphasize/Exclude Method involves first finding areas of study that
align with the goals you identified in the first part of your research. If you’re
learning French with the idea of going to Paris for two weeks and speaking in
shops and restaurants, I would focus a lot more on pronunciation than being able
to spell correctly. If you’re learning programming solely to make your own app,
I’d focus on the inner workings of app development more than theories of
computation.
The second part of the Emphasize/Exclude Method is to omit or delay
elements of your benchmarked curriculum that don’t align with your goals. For
example, one common recommendation for learning Mandarin Chinese,
advocated by people such as the renowned linguist and Sinologist Victor Mair, is
to focus on learning to speak before you try to read characters.
6 This isn’t the
only route available, but if your main goal is to speak, then this path to fluency
might be more effective.
How Much Planning Should You Do?
One question you may face is when to stop doing research and just get started.
The literature on self-directed learning, as typically practiced, demonstrates that
most people fail to do a thorough investigation of possible learning goals,
methods, and resources.
7
Instead they opt for whatever method of learning
comes up naturally in their environment. This clearly leaves a gap between what
is practiced and the efficiency that is possible using the best possible method.
However, research can also be a way of procrastinating, particularly if the
method of learning is uncomfortable. Just doing a bit more research then
becomes a strategy to avoid doing the work of learning. There will always be
some uncertainty in your approach, so it’s important to find the sweet spot
between insufficient research and analysis paralysis. You know when you’re
procrastinating, so just get started.
The 10 Percent Rule
A good rule of thumb is that you should invest approximately 10 percent of your
total expected learning time into research prior to starting. If you expect to spend
six months learning, roughly four hours per week, that would be equal to
roughly one hundred hours, which suggests that you should spend about ten
hours, or two weeks, doing your research. This percentage will decrease a little
bit as your project scales up, so if you plan to do five hundred or a thousand
hours of learning, I don’t think it necessarily demands fifty or a hundred hours of
research, but maybe closer to 5 percent of your time. The goal here isn’t to
exhaust every learning possibility but simply to make sure you haven’t latched
onto the first possible resource or method without thinking through alternatives.
Prior to starting my MIT Challenge, I spent roughly six months, part-time,
combing through all the course materials. A good idea is to be aware of the
common methods of learning, popular resources, and tools along with their
strengths and weaknesses before starting. Long projects provide more
opportunities for getting derailed and delayed, so doing proper research in the
opportunities for getting derailed and delayed, so doing proper research in the
beginning can easily save a much larger amount of time later on.
Diminishing Returns and Marginal Benefit Calculation
Metalearning research isn’t a onetime activity you do only before starting your
project. You should continue to do research as you learn more. Often obstacles
and opportunities aren’t clear before you start, so reassessing is a necessary step
of the learning process. During my portrait-drawing challenge, for instance, I
discovered about halfway that I was getting diminishing returns from my sketchand-compare method. I realized that I needed a better technique for drawing that
had higher accuracy. That led me to do a second round of research, leading to a
course taught by Vitruvian Studio, which detailed a more systematic method that
greatly increased my accuracy.
8
I hadn’t noticed it in my original research
because I wasn’t aware of the deficiency of my self-developed technique.
A more sophisticated answer to the question of when and how to do research
would be to compare the marginal benefits of metalearning to regular learning.
One way to do this is to spend a few hours doing more research—interviewing
more experts, searching online for more resources, searching for new possible
techniques—and then spend a few hours doing more learning along your chosen
path. After spending some time on each, do a quick assessment of the relative
value of the two activities. If you feel as though the metalearning research
contributed more than the hours spent on learning itself, you are likely at a point
where more research is still beneficial. If you felt that the extra research wasn’t
too helpful, you’re probably better off sticking to the plan you had before. This
type of analysis depends on something known as the Law of Diminishing
Returns. This states that the more time you invest in an activity (such as more
research), the weaker and weaker the benefits will be as you get closer and
closer to the ideal approach. If you keep doing research, eventually it will be less
valuable than simply doing more learning, so at that point you can safely focus
on learning. In practice, the return to research tends to be lumpy and variable.
You might spend a few hours and get nothing, then stumble onto the perfect
resource for accelerating your progress. As you finish more projects, it’s easier
to judge this point intuitively, but the Law of Diminishing Returns and the 10
Percent Rule can provide good approximations for how much research to do and
when.
Long-Term Prospects for Metalearning
So far we’ve talked only about the short-term benefits. However, the real
benefits of metalearning aren’t short term but long term. They don’t reside in a
particular project but influence your overall strengths as a learner.
Each project you do will improve your general metalearning. Every project
has the opportunity to teach you new learning methods, new ways to gather
resources, better time management, and improved skills for managing your
motivation. Success in one project will give you confidence to execute your next
one with boldness and without self-doubt and procrastination. Ultimately, this
effect far outweighs the effect of doing a specific project. Unfortunately, it’s also
something that can’t be boiled down to a tactic or tool. Long-term metalearning
is just something you acquire with experience.
The benefits of ultralearning aren’t always apparent from the first project
because that first project occurs when you’re at your lowest level of
metalearning ability. Each project you complete will give you new tools to
tackle the next, starting a virtuous cycle. Many of the ultralearners I interviewed
for this book told me a similar story: that they were proud of their
accomplishments in individual projects but that the real benefit had been that
they now understood the process of learning hard things. That gave them the
confidence to pursue other ambitious goals that they wouldn’t have even
considered previously. This confidence and ability are the ultimate goals of
ultralearning, even though they’re often hard to see from the outset. These
benefits, however, can be achieved only by putting in the work. The best
research, resources, and strategies are useless unless you follow up with
concentrated efforts to learn. That brings us to the next principle of ultralearning:
focus.
Chapter V
Principle 2
Focus
Sharpen Your Knife
Now I will have less distraction.
—Leonhard Euler, mathematician, upon losing the sight in his right eye
If ever there were an unlikely candidate for scientific greatness, it would have
been Mary Somerville. She was born into a poor Scottish family in the
eighteenth century, when higher education was not seen as proper for a lady. Her
mother did not prevent her from reading, but society at large did not approve of
it. An aunt, seeing that behavior, commented to her mother, “I wonder you let
Mary waste her time in reading, she never sews more than if she were a man.”
When she did have an opportunity to attend school briefly, her mother regretted
the expense. Somerville explained, “she would have been contented if I had only
learnt to write well and keep accounts, which was all that a woman was expected
to know.”
1 As a woman, she faced even larger obstacles, with household duties
and expectations taking precedence over any kind of self-education. “A man can
always command his time under the plea of business, a woman is not allowed
any such excuse,” she lamented. Her first husband, Samuel Greig, was strongly
against learning in women.
Yet despite those obstacles, Somerville’s accomplishments were vast. She
won awards in mathematics, learned several languages to fluency, and knew
how to paint and play the piano. In 1835, she, along with the German astronomer
Caroline Herschel, were the first women elected to the Royal Astronomical
Society. The accomplishment that eventually brought her fame was her
translation and expansion of the first two volumes of Pierre-Simon Laplace’s
Traité de mécanique céleste, a massive five-volume work on the theory of
gravitation and advanced mathematics, acclaimed as the greatest intellectual
achievement since Isaac Newton wrote the Principia Mathematica. Laplace
himself commented that Somerville was the only woman in the world who
understood his work.
The easiest explanation for the vast discrepancy in Somerville’s situation and
her accomplishments would be genius. It is no doubt true that she possessed an
incredibly sharp mind. Her daughter once commented that while she was being
taught, her mother could grow impatient. “I well remember her slender white
hand pointing impatiently to the book or slate—‘Don’t you see it? There is no
difficulty in it, it is quite clear.’” However, in reading through her descriptions of
her life, this seeming genius was beset by many insecurities. She claimed to have
“bad memory,” recounted struggles learning new things as a child, and had even
at one point “thought [herself] too old to learn to speak a foreign language.”
Whether that was polite modesty or genuine feelings of inadequacy, we cannot
know, but it does at least put cracks in the idea that she approached learning
from a place of unshakable confidence and talent.
Peering deeper, another picture of Somerville emerges. She had a keen
intellect, yes, but what she possessed in even greater quantities was an
exceptional ability to focus. As an adolescent, when she was put to bed and
denied a candle for reading, she would mentally work through the works of
Euclid in mathematics. While still breastfeeding her child, an acquaintance
encouraged her to study botany, so she devoted “an hour of study to that
science” every morning. Even during her greatest achievement, the translation
and expansion of Laplace’s Traité de mécanique céleste, she had to carry out all
the household duties of raising children, cooking, and cleaning. “I was always
supposed to be at home,” she explains, “and my friends and acquaintances came
so far out of their way on purpose to see me, it would have been unkind and
ungenerous not to receive them. Nevertheless, I was sometimes annoyed when in
the midst of a difficult problem one would enter and say, ‘I have come to spend
a few hours with you.’ However, I learnt by habit to leave a subject and resume
it again at once, like putting a mark into a book I might be reading.”
In the realm of great intellectual accomplishments an ability to focus quickly
and deeply is nearly ubiquitous. Albert Einstein focused so intensely during his
formulation of the general theory of relativity that he developed stomach
problems. The mathematician Paul Erdős was a heavy user of amphetamines to
increase his capacity for focus. When a friend bet him that he could not give
them up, even for a short time, he did manage to do so. Later, however, he
complained that the only result had been that mathematics as a whole was set
back a month in his unfocused absence. In these annals of extreme focus, one
often conjures up an image of solitary geniuses laboring away without
distraction, free from worldly concerns. However remarkable this is, I’m more
interested in the kind of focus that Somerville seemed to possess. How can one
in an environment such as hers, with constant distractions, little social support,
and continuous obligations, manage to focus long enough not only to learn an
impressive breadth of subjects, but to such depths that the French mathematician
Siméon Poisson once remarked that “there were not twenty men in France who
could read [her] book”?
How did Somerville become so good at focusing? What can we glean from
her strategies in getting difficult mental work done in less-than-ideal conditions?
The struggles with focus that people have generally come in three broad
varieties: starting, sustaining, and optimizing the quality of one’s focus.
Ultralearners are relentless in coming up with solutions to handle these three
problems, which form the basis of an ability to focus well and learn deeply.
Problem 1: Failing to Start Focusing (aka Procrastinating)
The first problem that many people have is starting to focus. The most obvious
way this manifests itself is when you procrastinate: instead of doing the thing
you’re supposed to, you work on something else or slack off. For some people,
procrastination is the constant state of their lives, running away from one task to
another until deadlines force them to focus and then having to struggle to get the
job done on time. Other people struggle with more acute forms of
procrastination that manifest themselves with particular kinds of tasks. I was
more like this second kind of person, where there were certain types of activities
I would spend all day procrastinating on. Though I have no problems writing
essays for my blog, when I had to do research for this book, I dragged my feet.
Similarly, I had no problem sitting and watching the videos of MIT classes, but I
Similarly, I had no problem sitting and watching the videos of MIT classes, but I
always tackled the first problem sets with considerable trepidation. Had it not
been for the intense schedule I was on, I might have found excuses to avoid
doing so for much longer. In fact, writing this chapter was one of the tasks I
procrastinated on a great deal.
Why do we procrastinate? The simple answer is that at some level there’s a
craving that drives you to do something else, there’s an aversion to doing the
task itself, or both. In my case, I procrastinated on writing this chapter because I
had a lot of ideas and I was unsure where to start. My anxiety was that by
committing something to paper, there was a good chance I might end up writing
it poorly. Silly, I know. But most motives to procrastinate are silly when you
verbalize them, yet that doesn’t stop them from ruling your life. Which brings
me to the first step to overcoming procrastination: recognize when you are
procrastinating.
Much procrastination is unconscious. You’re procrastinating, but you don’t
internalize it that way. Instead you’re “taking a much-needed break” or “having
fun, because life can’t always be about work all the time.” The problem isn’t
those beliefs. The problem is when they’re used to cover up the actual behavior
—you don’t want to do the thing you need to be focusing on, either because you
are directly averse to doing it or because there’s something else you want to do
more. Recognizing that you’re procrastinating is the first step to avoiding it.
Make a mental habit of every time you procrastinate; try to recognize that you
are feeling some desire not to do that task or a stronger desire to do something
else. You might even want to ask yourself which feeling is more powerful in that
moment—is the problem more that you have a strong urge to do a different
activity (e.g., eat something, check your phone, take a nap) or that you have a
strong urge to avoid the thing you should be doing because you imagine it will
be uncomfortable, painful, or frustrating? This awareness is necessary for
progress to be made, so if you feel as though procrastination is a weakness of
yours, make building this awareness your first priority before you try to fix the
problem.
Once you can easily and automatically recognize your tendency to
procrastinate, when it occurs, you can take steps to resist the impulse. One way
is to think in terms of a series of “crutches” or mental tools that can help you get
through some of the worst parts of your tendency to procrastinate. As you get
better about taking action on the project you’re working on, these crutches can
be changed or gotten rid of altogether when procrastination is no longer a
problem.
problem.
A first crutch comes from recognizing that most of what is unpleasant in a
task (if you are averse to it) or what is pleasant about an alternative task (if
you’re drawn to distraction) is an impulse that doesn’t actually last that long. If
you actually start working or ignore a potent distractor, it usually only takes a
couple minutes until the worry starts to dissolve, even for fairly unpleasant tasks.
Therefore, a good first crutch is to convince yourself to get over just the few
minutes of maximal unpleasantness before you take a break. Telling yourself
that you need to spend only five minutes on the task before you can stop and do
something else is often enough to get you started. After all, almost anyone can
endure five minutes of anything, no matter how boring, frustrating, or difficult it
may be. However, once you start, you may end up continuing for longer without
wanting to take the break.
As you progress, your first crutch may start to get in the way. You may find
yourself starting but then, because the task is unpleasant and focus is hard,
taking advantage of the five-minute rule too often to be productive. If this is the
case and your problem has switched from being unable to get started to taking
breaks too often, you can try something a little harder, say the Pomodoro
Technique: twenty-five minutes of focus followed by a five-minute break.*
Keep in mind that it’s essential not to switch to a harder goal when you’re still
mostly impeded by an earlier problem. If you still can’t start working, even with
the five-minute rule, switching to harder and more demanding crutches may
backfire.
In some cases, the moment of frustration may not come at the beginning, but
still be predictable. When I was learning Chinese characters through flash cards,
for instance, I’d always feel an urge to give up whenever I couldn’t remember
the answer to one of my cards. I knew this feeling was temporary, however, so I
added a rule for myself: I can only quit when I’ve remembered the most recent
card correctly. In practice, the cards were quick, so this usually only took an
extra twenty or thirty seconds of persistence; however, my patience for doing
flash cards went up dramatically as a result.
Eventually, if working on your project is not troubled by extreme
procrastination, you may want to switch to using a calendar on which you carve
out specific hours of your day in advance to work on the project. This approach
allows you to make the best use of your limited time. However, it works only if
you actually follow it. If you find yourself setting a daily schedule with chunked
hours and then frequently ignore it to do something else, go back to the start and
try building back up again with the five-minute rule and then the Pomodoro
Technique.
Technique.
Eventually, you may reach Mary Somerville’s level of focus, one that she
could activate on a moment-to-moment basis, making a decision as to whether
she had time to spare. Despite her formidable capacity for focus, it seems that
even Somerville would deliberately block out time for the study of particular
subjects. Therefore it was a conscious habit, not merely spontaneous studying,
that enabled her many successes. For myself, I find that some learning activities
are so intrinsically interesting that I can focus on them for a long time without
pressure. I generally had no problem watching lectures during the MIT
Challenge, for instance. Other tasks, however, required the five-minute rule for
me to get past my desire to procrastinate. If I had to scan and upload my files,
they’d often build up in a pile before I would finally tackle them. Don’t ever feel
bad if you have to back up a stage, either; you cannot control your aversions or
tendency to distraction, but with practice you can lessen their impact.
Problem 2: Failing to Sustain Focus (aka Getting
Distracted)
The second problem people tend to encounter is an inability to sustain focus.
This can happen when you’ve sat yourself down to study or practice something,
but then your phone buzzes and you look away, a friend knocks on the door to
say hello, or you spin off into a daydream only to realize you’ve been staring at
the same paragraph for the last fifteen minutes. Like the challenge of initiating
focus, sustaining focus is important if you want to make progress learning hard
things. Before I talk about how to sustain focus, however, I’d like to raise a
question about what kind of focus is the best to sustain.
Flow, a concept pioneered by the psychologist Mihály Csíkszentmihályi, is
often used as the model for what ideal focus looks like. This is the state of mind
you associate with being “in the zone.” You stop being interrupted by distracting
thoughts, and your mind becomes completely absorbed in the task at hand. Flow
is the enjoyable state that slides right between boredom and frustration, when a
task is neither too hard nor too easy. This rosy picture, however, does have some
detractors. The psychologist K. Anders Ericsson, the researcher behind
deliberate practice, argues that flow has characteristics that are “inconsistent
with the demands of deliberate practice for monitoring explicit goals and
feedback and opportunities for error correction. Hence, skilled performers may
enjoy and seek out flow experiences as part of their domain-related activities,
but such experiences would not occur during deliberate practice.”
2 Ultralearning,
with its similar focus on performance-driven learning, would also appear to be
unsuitable for flow, in the same way that Ericsson originally argued for
deliberate practice.
My own thought is that a flow state is not impossible during ultralearning.
Many cognitive activities associated with learning are in the range of difficulty
that makes flow possible or even likely. However, I also agree with Ericsson that
learning often involves entering into situations in which the difficulty makes
flow impossible. Additionally, the self-consciousness that is absent in flow may
need to be present in both ultralearning and deliberate practice, as you need to
consciously adjust your approach. Working on a programming problem at the
limit of your abilities, pushing yourself to write in a style that is unfamiliar to
you, or trying to minimize your accent when speaking a new language is each a
task that goes against the automatic patterns you may have accumulated. This
resistance to what is natural may make flow harder to achieve, even though it is
ultimately beneficial for accomplishing your learning goal.
My advice? Don’t worry about flow. In some learning tasks, you’ll achieve it
easily. I often felt as though I were in a flow state while doing practice problems
during the MIT Challenge, drilling vocabulary while learning languages, or
drawing. At the same time, don’t feel guilty if flow doesn’t come automatically.
Your goal is to enhance your learning, and this often involves pushing through
some sessions that are more frustrating than what could be considered ideal for
flow. Remember, even if your learning is intense, your use of the skill later on
will not be. Investments made in pushing through learning now will make
skillful practice a much more enjoyable activity down the road.
After considering how you should focus, let’s consider duration. How long
should you study? While this problem presumes that you’re getting distracted
and giving up focusing long before you should, the literature on focus does not
suggest that ever-longer periods of focus are optimal from a learning standpoint.
Researchers generally find that people retain more of what they learn when
practice is broken into different studying periods than when it is crammed
together. Similarly, the phenomenon of interleaving suggests that even within a
solid block of focus, it can make sense to alternate between different aspects of
the skill or knowledge to be remembered.
3 Therefore, if you have several hours
to study, you’re possibly better off covering a few topics rather than focusing
exclusively on one. Doing so has trade-offs, however, so if your study time
becomes more and more fractured, it may be difficult to learn at all.
What’s needed is a proper balance. To achieve it, fifty minutes to an hour is a
good length of time for many learning tasks. If your schedule permits only more
concentrated chunks of time, say once per week for several hours, you may want
to take several minutes as a break at the end of each hour and split your time
over different aspects of the subject you want to learn. Of course, these are
merely efficiency guidelines; you ultimately need to find what works best for
you, considering not only what is optimal for the purposes of retention but also
what fits your schedule, personality, and work flow. For some people, as little as
twenty minutes might fit their lives best; others may prefer to spend an entire
day learning.
Supposing that you’ve found a chunk of time to learn that is as optimal for
you as it can be, how can you sustain your focus during that time? I’ve found
that there are three different sources that cause focus to break down and
distraction to occur. If you’re struggling to concentrate, look at each of these
three in turn.
Distraction Source 1: Your Environment
The first source of distraction is your environment. Do you have your phone
turned off? Are you accessing the internet, watching television, or playing
games? Are there distracting noises and sounds? Are you prepared to work, or
might you need to stop to look for pens, a book, or a lamp? This is a source of
the problem of sustaining focus, but it’s also an aspect people frequently ignore
for the same reasons they ignore the fact that they are procrastinating. Many
people tell themselves that they focus better while listening to music, let’s say,
but the reality might be that they don’t want to work on a given task, so music
provides a low-level, amusing distraction. This isn’t to condemn anyone who
doesn’t work in a perfect environment. I certainly don’t. Rather, be aware of
what environment you work best in, and test it. Do you actually get more work
done with the television on in the background, or do you just like hearing the
television and feel that it makes the work more bearable? If it’s the latter, you
can probably train yourself to avoid multitasking and enjoy greater productivity.
Multitasking may feel like fun, but it’s unsuitable for ultralearning, which
requires concentrating your full mind on the task at hand. It’s better to rid
yourself of this vice than to strengthen bad habits of ineffective learning.
Distraction Source 2: Your Task
The second source is the task you’re trying to learn. Certain activities, due to
their nature, are harder to focus on than others. I find reading harder to focus on
than watching a video, even when the content is the same. Whenever you have a
choice between using different tools for learning, you may want to consider
which is easier to focus on when making that decision. This choice of materials
shouldn’t supersede other considerations—I wouldn’t opt for a tool that is much
less direct (Principle 3) or offers no feedback (Principle 6), simply for the sake
of greater focus. Fortunately, these principles are generally aligned, and it is
actually the somewhat less effective methods that are less cognitively demanding
and therefore harder to sustain focus on. Sometimes you can subtly modify what
you’re doing to enable greater focus. If I have difficult reading to do, I will often
make an effort to jot down notes that reexplain hard concepts for me. I do this
mostly because, while I’m writing, I’m less likely to enter into the state of
reading hypnosis where I’m pantomiming the act of reading while my mind is
actually elsewhere. More intense strategies, whether solving problems, making
something, or writing and explaining ideas aloud, are harder to do in the
background of your mind, so there are fewer opportunities for distractions to
creep in.
Distraction Source 3: Your Mind
The third source is your mind itself. Negative emotions, restlessness, and
daydreaming can be some of the biggest obstacles to focus. This problem has
two sides. First, it’s obvious that a clear, calm mind is best for focusing on
almost all learning problems. A mind filled with angers, anxieties, frustrations,
or sadness will be harder to study with. This means that if you’re struggling with
problems in your life, you’ll have a harder time learning well, and you may want
to look at dealing with those first. Being in a toxic relationship, having anxiety
about some other task you’re procrastinating on, or simply knowing you’re
going down the wrong road in life can interfere with your motivation, so it’s
often best not to ignore these issues. However, sometimes there’s nothing you
can do about your emotions, and feelings arise spontaneously without requiring
you to do something about them. A random worry about some future event
might bubble up, let’s say, but you know you shouldn’t stop the activity you’re
working on right now in order to deal with it. Here the solution is to
working on right now in order to deal with it. Here the solution is to
acknowledge the feeling, be aware of it, and gently adjust your focus back to
your task and allow the feeling to pass.
Allowing negative feelings to pass, of course, is a lot easier said than done.
Emotions can hijack the mind and make the process of returning awareness to
your project feel like a Sisyphean task. If I’m really anxious about something,
for instance, I may feel as though I’m returning my attention to a task, only for it
to jump away fifteen seconds later, repeating again and again for an hour or
more. In such moments, recognize that by not reacting to the emotion at the level
of abandoning your task entirely, you’ll diminish its intensity in the future.
You’ll also strengthen your commitment to continue working in future situations
like this, so they will become easier. Mindfulness researcher and psychiatrist
Susan Smalley and meditation teacher Diana Winston of UCLA’s Mindful
Awareness Research Center argue that when we are engaging in a behavior, our
typical reaction is to try to suppress distracting thoughts. If instead you “learn to
let it arise, note it, and release it or let it go,” this can diminish the behavior
you’re trying to avoid.
4
If it ever feels as though continuing working is pointless
because you’re so distracted by a negative emotion that you can’t possibly work,
remember that the long-term strengthening of your ability to persist on this task
will be useful, so the time is not wasted even if you don’t accomplish much in
this particular learning session.
Problem 3: Failing to Create the Right Kind of Focus
A third, problem, subtler than the other two, has to do with the quality and
direction of your attention. Supposing you’ve managed to wrangle the problems
of procrastination and distraction down long enough to focus on your task, how
should you do it? What’s the optimal degree of alertness to maximize your
learning?
Here there is some interesting research relating two different variables,
arousal and task complexity, to the question of how you should apply your
attention. Arousal (the general, not sexual, variety) is your overall feeling of
energy or alertness. When you’re sleepy, you have low arousal; when you’re
exercising, you have high arousal. This bodily phenomenon occurs due to
sympathetic nervous system activation, and it consists of a range of effects in the
body that often occur together, including faster heart rate, increased blood
pressure, pupil dilation, and sweating. Mentally, arousal also influences
attention. High arousal creates a feeling of keen alertness, which is often
characterized by a fairly narrow range of focus, but one that can also be
somewhat brittle.
5 This can be very good for focusing on relatively simple tasks
or ones that require intense concentration toward a small target. Athletes require
this kind of concentration to throw a dart at a target or shoot a basketball
properly, where the task is fairly simple but requires concentration to execute
properly. Too much arousal, however, and focus starts to suffer.
6
It becomes
very easy to be distracted, and you may have a hard time holding focus at any
particular spot. Anyone who’s drunk too much coffee and feels jittery knows
how this can impact your work.
More complex tasks, such as solving math problems or writing essays, tend to
benefit from a more relaxed kind of focus.
7 Here the space of focus is often
larger and more diffuse. This has advantages when, in order to solve the problem
you’re facing, you must consider many different inputs or ideas. Trying to solve
a complex math problem or write a love sonnet is likely to require this mental
quietness. When doing a particularly creative task, if you get stuck, you may
benefit from no focus at all.
8 Taking a break from the problem can widen the
space of focus enough that possibilities that were not in your consciousness
earlier can conjoin and you can make new discoveries. This is a scientific
explanation of “Eureka!” moments occurring during leisure or while falling
asleep, instead of while at work. Still, before you begin to think that sloth is the
key to creativity, it’s clear that such an approach often only works when one has
been focusing on a problem for long enough that the residue of ideas remains in
one’s mind. Not working at all is unlikely to lead to creative genius, but taking a
break may help breathe fresh perspective into a hard problem.
The relationship between task complexity and arousal is interesting because
the latter can be modified. In one experiment, sleep-deprived and well-rested
subjects worked on a cognitive task.
9 Unsurprisingly, the sleepy subjects didn’t
do as well. More interesting, however, was that the sleepy subjects did better
when a loud noise was played in the background, while the well-rested subjects
did worse. The conclusion drawn by the researchers was that the noise increased
arousal levels, which benefited the low-arousal sleepy subjects, but it increased
arousal too much for the well-rested ones, causing their decline in performance.
This implies that you may want to consider optimizing your arousal levels to
sustain the ideal level of focus. Complex tasks may benefit from lower arousal,
so working in a quiet room at home might be the right idea for math problems.
Simpler tasks might benefit from a noisier environment, say working at a coffee
shop. This laboratory experiment shows that you should find out what works
best for your own ability to focus through self-testing. You may find you can
work better on complex tasks even in the noisy coffee place, or you may find
that even for simple tasks you need the quiet room in the library.
Improving Your Ability to Focus
Focus doesn’t need to be exclusive to the domain of those who have endless
hours and large swaths of free time on their schedule. As was the case with
Somerville, the ability to focus is even more important for those whose lives
make such large commitments of time impossible. With practice you can
improve your ability to focus. I’m agnostic about whether focus can be trained as
an ability, in general. Just because you’re disciplined about one thing doesn’t
automatically make you disciplined about everything else. However, what does
generalize is that there is a procedure you can follow to get better at focusing.
My advice is this: recognize where you are, and start small. If you’re the kind of
person who can’t sit still for a minute, try sitting still for half a minute. Half a
minute soon becomes one minute, then two. Over time, the frustrations you feel
learning a particular subject may become transmuted into genuine interest. The
impulse to engage in distractions will weaken each time you resist it. With
patience and persistence, your few minutes may become large enough to
accomplish great things, just as Somerville did almost two hundred years ago.
Now that we’ve discussed how to get started on learning hard things, let’s
move to discussing the right way to learn them. The next principle, directness, is
the first to explain what types of things you should do while learning and, more
important, which you should avoid if you want to be able to use what you learn.
Chapter VI
Principle 3
Directness
Go Straight Ahead
He who can go to the fountain does not go to the water jar.
—Leonardo da Vinci
After growing up in India, Vatsal Jaiswal moved to Canada with the dream of
becoming an architect. Now, four years later, armed with a newly minted degree
and entering into the worst job market since the Great Depression, that dream
was beginning to seem very far away. Getting a foothold in architecture can be
difficult, even in good economic times. But just a few years out from the market
crash of 2007, it was nearly impossible. Firms were laying off even experienced
architects. If anyone was hiring, they weren’t taking chances on some kid just
out of school. Out of his graduating class, almost nobody had found an
architecture job yet. Most had given up, taking jobs outside the field, going back
for more education, or moving in with parents until the economic storms abated.
Another rejection. Jaiswal leaves the offices of yet another architecture firm,
walking back to his sliver of the one-bedroom apartment he shares with two
roommates.
1 After hundreds of résumés submitted with no reply, he’s moved on
to trying a more aggressive tactic of going directly to a firm’s offices, pleading
to speak with whomever is in charge. Still, after weeks of knocking on doors and
making dozens of unsolicited office visits, there’s no job offer in sight. He hasn’t
even gotten a call back for a single interview.
Still, Jaiswal suspected that his struggles could be blamed on more than just
the recession. From the snippets of feedback he could pry out of the places he
applied to, he sensed that the companies didn’t see him as a useful employee. He
had studied architecture in school, but his program had focused mostly on design
and theory. He had been trained in creative design projects that were isolated
from the reality of building codes, construction costs, and tricky software.
Because his portfolio of school projects didn’t resemble the detailed technical
documents the architects worked with, they thought hiring him would involve a
lengthy training period, something few firms could currently afford.
Jaiswal needed to come up with a plan. More résumé submissions and office
walk-ins weren’t going to work. He needed a new portfolio that could prove he
had the exact skills firms wanted. He needed to show them that, rather than
being a burden, he could get to work straightaway and be a valuable team
member from the first day.
To do this, he would need to know more about how architects actually drew
plans for buildings—not just the big theories and designs, which he had learned
in school, but little details of how they did their drawings, what codes they used
to represent different materials, and what the drawings showed and omitted. To
do that, he found a job at a large-form print shop, the kind that does printing on
the large sheets of paper favored for architectural blueprints. Low paying and
low skilled, a job in a print shop wasn’t Jaiswal’s end goal. Still, it could help
him scrape by financially while he prepared his new portfolio. Even better, the
print store gave him daily exposure to the blueprints firms were using. That
allowed him to absorb countless details about how the drawings were put
together.
Next, Jaiswal would need to upgrade his technical skills. From his walk-in
visits, he was aware that many of the firms he was applying at were using a
complex design software called Revit. If he could master its ins and outs, he
thought, he could be immediately useful in the technology-heavy entry-level
position he desired. At night, he pushed through online tutorials and taught
himself the software.
Finally, he was ready to construct a new portfolio. Combining his new Revit
knowledge with the knowledge of architectural drawings he had gained while
working at the print shop, he made a new portfolio. Instead of the assorted
projects from university, he focused on a single building of his own design: a
projects from university, he focused on a single building of his own design: a
three-tower residential structure with raised courtyards and a modern aesthetic.
The project pushed his skills with software further, forcing him to learn new
methods and ideas beyond the basics of his online tutorials and exposure at the
print shop. Eventually, after a few months of work, he was ready.
New portfolio in hand, Jaiswal submitted it again, this time to just two
architecture firms. To his surprise, they both immediately offered him a job.
The Importance of Being Direct
Jaiswal’s story perfectly illustrates the third principle of ultralearning: directness.
By seeing how architecture was actually being done and learning a set of skills
that was closely related to the job position he wanted to perform, he was able to
cut through the swaths of recent graduates with unimpressive portfolios.
Directness is the idea of learning being tied closely to the situation or context
you want to use it in. In Jaiswal’s case, when he wanted to get enough
architectural skill that firms would hire him, he opted to build a portfolio using
the software those firms used and design in the style those firms practiced. There
are many routes to self-education, but most of them aren’t very direct. In
contrast to Jaiswal, another architect I spoke with aimed to improve his
employability by deepening his knowledge of design theories. Though that
might have been interesting and fun, it was disconnected from the actual skills
he would be using in entry-level work. Just as Jaiswal struggled to get work with
his university portfolio, many of us are building the wrong portfolio of skills for
the kinds of career and personal achievements we want to create. We want to
speak a language but try to learn mostly by playing on fun apps, rather than
conversing with actual people. We want to work on collaborative, professional
programs but mostly code scripts in isolation. We want to become great
speakers, so we buy a book on communication, rather than practice presenting.
In all these cases the problem is the same: directly learning the thing we want
feels too uncomfortable, boring, or frustrating, so we settle for some book,
lecture, or app, hoping it will eventually make us better at the real thing.
Directness is the hallmark of most ultralearning projects.* Roger Craig did his
Jeopardy! testing on the actual questions from past shows. Eric Barone learned
video game art by making art for his video game. Benny Lewis learns to speak
languages quickly by following a policy of attempting some back-and-forth
dialog from the very first day. What these approaches share is that the learning
activities are always done with a connection to the context in which the skills
learned will eventually be used.
The opposite of this is the approach so often favored in more traditional
classroom-style learning: studying facts, concepts, and skills in a way that is
removed from how those things will eventually be applied: mastering formulas
before you understand the problem they’re trying to solve; memorizing the
vocabulary of a language because it’s written on a list, not because you want to
use it; solving highly idealized problems that you’ll never see again after
graduation.
Indirect approaches to learning, however, aren’t limited to traditional
education. Many self-directed learners fall into the trap of indirect learning.
Consider Duolingo, currently one of the most popular language-learning
applications. On the surface, there’s a lot to like about this app. It’s colorful and
fun and gives you a potent sense of progress. But I suspect that much of the
sense of progress is an illusion, at least if your goal is to eventually be able to
speak the language. To understand why, consider how Duolingo encourages you
to practice. It provides English words and sentences and then asks you to pick
words from a word bank to translate them.* The problem is that this is nothing
like actually speaking a language! In real life, you may start by trying to
translate an English sentence into the language you want to learn. However, real
speaking situations don’t present themselves as a multiple choice. Instead, you
have to dredge up the actual words from memory or find alternative words if you
haven’t learned one of the ones you want to use. This is, cognitively speaking,
quite a different task from picking out matching translations from a highly
limited word bank, and also much more difficult. Benny Lewis’s method of
speaking from the start may be hard, but it transfers perfectly to the task he
eventually wants to become good at: having conversations.
During the MIT Challenge, I recognized that the most important resource for
being able to eventually pass the classes wasn’t having access to recorded
lectures, it was having access to problem sets. Yet, in the years since this project,
when I am asked for help by students, they often decry the absence of lecture
videos from some classes, only rarely complaining about incomplete or
insufficient problem sets. This makes me think that most students view sitting
and listening to a lecture as the main way that they learn the material, with doing
problems that look substantially similar to those on the final exam as being a
superficial check on their knowledge. Though first covering the material is often
essential to begin doing practice, the principle of directness asserts that it’s
essential to begin doing practice, the principle of directness asserts that it’s
actually while doing the thing you want to get good at when much of learning
takes place. The exceptions to this rule are rarer than they may first appear, and
therefore directness has been a thorny problem in the side of education for over a
century.
The easiest way to learn directly is to simply spend a lot of time doing the
thing you want to become good at. If you want to learn a language, speak it, as
Benny Lewis does. If you want to master making video games, then make them,
as Eric Barone does. If you want to pass a test, practice solving the kinds of
problems that are likely to appear on it, as I did in my own MIT Challenge. This
style of learning by doing won’t work for all projects. The “real” situation may
be infrequent, difficult, or even impossible to create, and thus learning in a
different environment is unavoidable. Roger Craig couldn’t practice Jeopardy!
by being on the show hundreds of times. He knew he had to learn in a different
environment and prepare to transfer that knowledge to the show when it came
time to do so. In such situations, directness isn’t an all-or-nothing feature but
something you can gradually increase to improve your performance. Craig’s
approach to start by learning from actual past Jeopardy! questions was a lot
more effective than if he had just started learning trivia from random topics.
Jaiswal was similarly limited when learning architectural skills, as the places he
wanted to work wouldn’t hire him. However, he worked around that by training
on the same software they used and designing a portfolio that was based on the
same types of drawings and renderings that were done in actual practice. The
twin challenge of directness is that sometimes the exact situation in which you
want to use the skill isn’t available for easy practice. Even if you can go straight
into learning by doing, this approach is often more intense and uncomfortable
than passively watching lecture videos or playing around with a fun app. If you
don’t pay attention to directness, therefore, it’s very easy to slip into lousy
learning strategies.
One of the big takeaways of Jaiswal’s story might not be the triumph of his
self-directed learning project but the failure of his formal education. After all, his
difficulties started after he had already spent four years studying architecture
intensely at university. Why, then, would such a small project, postgraduation,
make such a large difference in his employability? To answer that, I’d like to
turn to one of the most stubborn and disturbing problems in educational
psychology: the problem of transfer.
Transfer: Education’s Dirty Secret
Transfer has been called the “Holy Grail of education.” It happens when you
learn something in one context, say in a classroom, and are able to use it in
another context, say in real life. Although this may sound technical, transfer
really embodies something we expect of almost all learning efforts—that we’ll
be able to use something we study in one situation and apply it to a new
situation. Anything less than this is hard to describe as learning at all.
Unfortunately, transfer is also something that, despite more than a century of
intense work and research, has largely failed to occur in formal education. The
psychologist Robert Haskell has said in his excellent coverage of the vast
literature on transfer in learning, “Despite the importance of transfer of learning,
research findings over the past nine decades clearly show that as individuals, and
as educational institutions, we have failed to achieve transfer of learning on any
significant level.” He later added, “Without exaggeration, it’s an education
scandal.”
2
The situation is even more disturbing than it sounds. Haskell pointed out, “We
expect that there will be transfer of learning, for example, from a high school
course in introductory psychology to a college-level introduction to psychology
course. It has been known for years, however, that students who enter college
having taken a high school psychology course do no better than students who
didn’t take psychology in high school. Some students who have taken a
psychology course in high school do even worse in the college course.” In
another study, college graduates were asked questions about economic issues
and no difference in performance was found between those who had taken an
economics class and those who had not.
3
Providing multiple examples seems to aid transfer a bit, yet the cognitive
science researcher Michelene Chi noted that “in almost all the empirical work to
date, on the role of example solutions, a student who has studied examples often
cannot solve problems that deviate slightly from the example solution.”
4
In his
book The Unschooled Mind: How Children Think and How Schools Should
Teach, the developmental psychologist Howard Gardner pointed to the body of
evidence showing that even “students who receive honors grades in college-level
physics courses are frequently unable to solve basic problems and questions
encountered in a form slightly different from that on which they have been
formally instructed and tested.”
5 Nor has this failure of transfer been limited to
schools. Corporate training also suffers, with the former Times Mirror Training
Group chairman John H. Zenger writing “Researchers who rigorously evaluate
training have said that demonstrable changes following training are hard to
find.”
6
The recognition of the failure of general transfer has a history as long as the
study of the problem itself. The first attack on the problem came from the
psychologists Edward Thorndike and Robert Woodworth in 1901, with their
seminal paper “The Influence of Improvement in One Mental Function upon the
Efficiency of Other Functions.” In it, they attacked the dominant theory of
education at the time, so-called formal discipline theory. This theory suggested
that the brain was analogous to a muscle, containing fairly general capacities of
memory, attention, and reasoning, and that training those muscles, irrespective
of the content, could result in general improvement. This was the predominant
theory behind universal instruction in Latin and geometry, on the idea that it
would help students think better. Thorndike was able to refute this idea by
showing that the ability to transfer was much narrower than most people had
assumed.
Although studying Latin has fallen out of favor, many educational pundits are
reviving new incarnations of the formal discipline theory by suggesting that
everyone learn programming or critical thinking in order to improve their
general intelligence. Many popular “brain-training” games also subscribe to this
view of the mind, assuming that deep training on one set of cognitive tasks will
extend to everyday reasoning. It’s been more than one hundred years since the
verdict came in, yet the allure of a general transfer procedure still has many
searching for the Holy Grail.
Despite all this, the situation isn’t without hope. Although empirical work and
educational institutions have often failed to demonstrate significant transfer, it is
not the case that transfer doesn’t exist. Wilbert McKeachie, in reviewing the
history of transfer, noted that “Transfer is paradoxical. When we want it, we do
not get it. Yet it occurs all the time.”
7 Whenever you use an analogy, saying
something is like something else, you’re transferring knowledge. If you know
how to ice skate and later learn to Rollerblade, you’re transferring skills. As
Haskell pointed out, if transfer were really impossible, we would be unable to
function.
So what explains the disconnect? Why have educational institutions struggled
to demonstrate significant transfer, if transfer is something we all need to
to demonstrate significant transfer, if transfer is something we all need to
function in the world? Haskell suggests that a major reason is that transfer tends
to be harder when our knowledge is more limited. As we develop more
knowledge and skill in an area, they become more flexible and easier to apply
outside the narrow contexts in which they were learned. However, I’d like to add
my own hypothesis as an explanation for the transfer problem: most formal
learning is woefully indirect.
Overcoming the Problem of Transfer with Directness
Directness solves the problem of transfer in two ways. The first and most
obvious is that if you learn with a direct connection to the area in which you
eventually want to apply the skill, the need for far transfer is significantly
reduced. Given a century of research showing the difficulties of transfer along
with proposed solutions that have failed to provide lasting results, any student
must take seriously the notion that transferring what has been learned between
very different contexts and situations will be treacherous. If our learning is, as
Haskell suggests, “welded to a place or subject matter,” it is a lot better that
those situations be close to the ones we actually want to use.
Second, I believe that directness may help with transfer to new situations,
beyond its more obvious role in preventing the need for far transfer. Many reallife situations share many subtle details with other real-life situations that they
never share with the abstract environment of the classroom or textbook.
Learning something new rarely depends just on the mass of easily articulated
and codified knowledge present but on the myriad tiny details of how that
knowledge interacts with reality. By learning in a real context, one also learns
many of the hidden details and skills that are far more likely to transfer to a new
real-life situation than from the artificial environment of a classroom. Using a
personal example, one of the skills I found most important in the no-English
project was being able to quickly use a dictionary or translation app on my
phone, so I could fill gaps in my linguistic knowledge in midconversation.
However, it’s exactly this kind of practical skill that is rarely covered in a
language-learning curriculum. While this is a trivial example, real-life situations
contain thousands of such pieces of skill and knowledge that are necessary if
you’re going to apply academically learned subjects in the real world.
Ultimately, it will be for researchers to decide whether the Holy Grail of
education will ever be found. In the meantime, as learners, we must accept that
education will ever be found. In the meantime, as learners, we must accept that
initial learning efforts often stick stubbornly to the situations we learn them in.
The programmer who learns about an algorithm from a class may have trouble
recognizing when to use it in her code. The leader who learns a new
management philosophy from a business book may go back to working with the
same approach she has always used with her employees. My favorite example,
though, has to be when a group of friends invited me to join them at a casino. I
asked them if their studies ever kept them from enjoying gambling, and they just
looked at me blankly. I thought it was funny because the students were actuaries.
Their years spent studying statistics in the classroom should have convinced
them that you can’t expect to beat the house, yet that connection didn’t seem to
dawn on them. When we learn new things, therefore, we should always strive to
tie them directly to the contexts we want to use them in. Building knowledge
outward from the kernel of a real situation is much better than the traditional
strategy of learning something and hoping that we’ll be able to shift it into a real
context at some undetermined future time.
How Ultralearners Avoid the Problem of Transfer and
Learn Directly
Given the problem of transfer and the importance of learning directly, let’s look
at some of the ways that this is managed in different ultralearning projects. The
simplest way to be direct is to learn by doing. Whenever possible, if you can
spend a good portion of your learning time just doing the thing you want to get
better at, the problem of directness will likely go away. If this isn’t possible, you
may need to create an artificial project or environment to test your skills. What
matters most here is that the cognitive features of the skill you’re trying to
master and the way you practice it be substantially similar. Consider again
Craig’s simulation of Jeopardy! games by doing questions from old tests. The
fact that he was using actual past questions is more important than whether his
program matched the signature blue background color present on the show’s
display. This is because the background color didn’t provide any information
that would have changed his responses to the questions. The skill he was
practicing wasn’t changed much by it. In contrast, if he had taken trivia
questions from a different game (say the board game Trivial Pursuit) there might
have been differences in how questions are typically asked, the topics they are
drawn from, or the difficulty level. Worse, if he had spent all his time reading
random Wikipedia articles to learn trivia, he wouldn’t have been practicing the
fundamental skill of recalling answers based on cryptic Jeopardy!-style clues at
all.
In other cases, what you’re trying to achieve may not be a practical skill.
Many of the ultralearners I encountered wanted, as their end goal, to understand
a subject particularly well, such as Vishal Maini with machine learning and
artificial intelligence. Even my own MIT Challenge was based around gaining a
deep understanding of computer science, as opposed to a more practical goal of
building an app or video game. Though this may seem like a case where
directness no longer matters, that really isn’t true. It’s simply that the place you
want to apply these ideas is less obvious and concrete. In Maini’s case, he
wanted to be able to think and talk intelligently about machine learning, enough
to be able to land a nontechnical role in a company that utilized those methods.
That meant that being able to communicate his ideas articulately, understanding
the concepts clearly, and being able to discuss them with both knowledgeable
practitioners and laypeople was important. That’s why his goal to make a
minicourse explaining the basics of machine learning fit so well. His learning
was directly connected with where he wanted to apply the skill: communicating
it to others.
Although the findings of the research on transfer are fairly bleak, there is a
glimmer of hope, which is that gaining a deeper knowledge of a subject will
make it more flexible for future transfer. Whereas the structures of our
knowledge start out brittle, welded to the environments and contexts we learn
them in, with more work and time they can become flexible and can be applied
more broadly. This is the conclusion of Robert Haskell, and although it does not
provide a short-term solution to the problem for new learners, it does suggest a
path out for those who want to continue working on a subject until they master
it. Many ultralearners who have specialized in a smaller subset of fields are
masters at transfer; no doubt this is largely due to their depth of knowledge,
which makes transfer easier to accomplish. Dan Everett, who was featured in the
opening of the chapter on the first principle, metalearning, is a prime example of
this. His linguistic depth allows him to learn new languages relatively easily,
compared to someone who has learned only a second language or has learned
only languages academically.
How to Learn Directly
How to Learn Directly
Given the well-documented difficulty with indirect forms of learning, why are
they still the default both in schools and in many failed attempts at selfeducation? The answer is that learning directly is hard. It is often more
frustrating, challenging, and intense than reading a book or sitting through a
lecture. But this very difficulty creates a potent source of competitive advantage
for any would-be ultralearner. If you’re willing to apply tactics that exploit
directness despite these difficulties, you will end up learning much more
effectively.
Let’s examine some of the tactics ultralearners use to maximize this principle
and take advantage of the inadequacies of more typical schooling.
Tactic 1: Project-Based Learning
Many ultralearners opt for projects rather than classes to learn the skills they
need. The rationale is simple: if you organize your learning around producing
something, you’re guaranteed to at least learn how to produce that thing. If you
take classes, you may spend a lot of time taking notes and reading but not
achieve your goal.
Learning to program by creating your own computer game is a perfect
example of project-based learning. Engineering, design, art, musical
composition, carpentry, writing, and many other skills naturally lend themselves
to projects that produce something at the end. However, an intellectual topic can
also be the basis of a project. One ultralearner I interviewed, whose project is
still ongoing, wanted to learn military history. His project, in this case, was to
work toward producing a thesis paper. Since his end goal was to be able to
converse knowledgeably about the subject, a project to produce an original paper
applied learning more directly than simply trying to read a lot of books without
creating anything.
Tactic 2: Immersive Learning
Immersion is the process of surrounding yourself with the target environment in
which the skill is practiced. This has the advantage of requiring much larger
amounts of practice than would be typical, as well as exposing you to a fuller
range of situations in which the skill applies.
range of situations in which the skill applies.
Learning a language is the canonical example of where immersion works. By
immersing yourself in an environment where a language is spoken, not only do
you guarantee that you’ll end up practicing the language a lot more than you
would otherwise (since you have no choice), but you also face a broader
diversity of situations that require learning new words and phrases. However,
language learning is not the only place where you can apply immersion to learn
more. Joining communities of people who are actively engaged in learning can
have a similar impact, since it encourages constant exposure to new ideas and
challenges. For example, novice programmers might join open-source projects to
expose themselves to new coding challenges.
Tactic 3: The Flight Simulator Method
Immersion and projects are great, but for many skills there’s no way to actually
practice the skill directly. For skills such as piloting a plane or performing
surgery, it’s not even legal to practice them in a real situation until you’ve
already invested considerable time into training. How can you overcome this?
It’s important to note that what matters for transfer is not every possible
feature of the learning environment, such as what room you’re in or what clothes
you’re wearing while you learn. Rather, it’s the cognitive features—situations
where you need to make decisions about what to do and cue knowledge you’ve
stored in your head. This suggests that when direct practice is impossible, a
simulation of the environment will work to the degree to which it remains
faithful to the cognitive elements of the task in question. For flying a plane, this
means that practicing on a flight simulator may be as good for learning as flying
an actual plane if it sufficiently calls on the discriminations and decisions a pilot
needs to make. Better graphics and sounds aren’t important, unless they change
the nature of the decisions being made or the cues pilots receive for when to use
certain skills or knowledge.
8
When evaluating different methods for learning, those that significantly
simulate the direct approach will transfer a lot better. Therefore, if you’re trying
to evaluate what’s the best way to learn French before your trip to France, you’ll
get more (although not perfect) transfer from doing Skype tutoring than you will
from flipping through flash cards.
Tactic 4: The Overkill Approach
The last method I’ve found for enhancing directness is to increase the challenge,
so that the skill level required is wholly contained within the goal that is set.
Tristan de Montebello, when preparing to compete in the World Championship
of Public Speaking, pushed to speak at middle schools, giving early versions of
his talk. His feeling was that the feedback he received at Toastmasters clubs
might be too soft or congratulatory to cut deep at what worked and didn’t work
in his speech. Middle school students, in contrast, would be merciless. If a joke
he said wasn’t funny or his delivery was boring or cheesy, he would be able to
tell immediately from their faces what needed to be reworked. The overkill
approach is to put yourself into an environment where the demands are going to
be extremely high, so you’re unlikely to miss any important lessons or feedback.
Going into this environment can feel intense. You may feel as though you’re
“not ready” to start speaking a language you’ve barely learned. You may be
afraid to stand onstage and deliver a speech you haven’t memorized perfectly.
You might not want to dive right into programming your own application and
prefer to stick to watching videos where someone else does the coding. But these
fears are often only temporary. If you can get enough motivation to start this
method, it’s often a lot easier to continue it long term. The first week in each
new country in my language learning project was always a shock, but soon it
became completely normal to live entirely within the new language.
One way you can overkill a project is to aim for a particular test, performance,
or challenge that will be above the skill level you strictly require. Benny Lewis
likes to attempt language exams, because they provide a concrete challenge. In
his German project, he wanted to attempt the highest-level exam, because his
awareness of that goal would push him to study more than he might if he were
satisfied with comfortable conversations alone. Another friend of mine decided
to exhibit her photography as a means of pushing her skills and talent. Deciding
in advance that your work will be viewable publicly alters your approach to
learning and will gear you toward performance in the desired domain, rather
than just checking off boxes of facts learned.
Learn Straight from the Source
Learning directly is one of the hallmarks of many of the successful ultralearning
projects I’ve encountered, particularly because of how different it can be from
the style of education most of us are used to. Whenever you learn anything new,
it’s a good habit to ask yourself where and how the knowledge will manifest
it’s a good habit to ask yourself where and how the knowledge will manifest
itself. If you can answer that, you can then ask whether you’re doing anything to
tie what you’re learning to that context. If you’re not, you need to tread
carefully, as the problem of transfer may rear its ugly head.
The act of learning directly, however, is only half of the answer to the
question of what you should do to learn well. Doing a lot of direct practice in the
environment where you want to eventually use your skills is an important start.
However, in order to master skills quickly, bulk practice isn’t enough. This
brings us to our next principle of ultralearning: drill.
Chapter VII
Principle 4
Drill
Attack Your Weakest Point
Take care of the bars and the piece will take care of itself.
—Philip Johnston, composer
Of all the roles Benjamin Franklin played throughout his life—entrepreneur,
inventor, scientist, diplomat, and founding father of the United States—he was
first and foremost a writer. It was in writing that he first found success. After
fleeing Boston to escape the final years of his indentured labor as an apprentice
to his brother’s printing company, he went to Philadelphia. There, penniless and
unknown, he first worked for another printing company before establishing
himself as a competitor. His Poor Richard’s Almanack became an international
bestseller and allowed him to retire at forty-two. However, it was in the latter
half
1 of his life that his writing would have world-changing consequences.
2
As a scientist, Franklin was bad at math and more interested in practical
consequences than in grand theories of the universe. However, his prose was
“written equally well for the uninitiated as well as the philosopher,” noted the
English chemist Sir Humphrey Davy, adding “he has rendered his details as
amusing as well as perspicuous.”
3 The strength of his writing and its practical
consequences made him an international sensation.
In politics, it was again Franklin’s writing talent that helped him win allies
and persuade potential antagonists. Prior to the American Revolution, he penned
an essay, supposedly written by King Frederick II of Prussia, entitled “An Edict
by the King of Prussia.” In it he satirized British-American relations by
proposing that, due to early settlers of the British Isles being of German origin,
“revenue [should] be raised from said colonies in Britain” by the Prussian king.
Later, his skill with a pen would make his writing into the Declaration of
Independence, where he edited Thomas Jefferson’s words to become the now
famous “We hold these truths to be self-evident.”
With such an amazing skill for writing and persuasion, it’s worth asking how
Franklin acquired it. Fortunately, unlike so many other great writers, whose
efforts at honing their skills remain mysterious, we have Franklin’s own words
for how he did it. In his Autobiography, he details his sophisticated efforts to
slice apart sections of his writing skill for practice as a young boy. Beginning
with a childhood debate against a friend about the merits of educating women
(Franklin was for, his friend against), his father noticed that aspects of his
writing lacked persuasive ability. Franklin thus “determined to endeavor at
improvement” and went about a series of exercises to practice his writing skill.
One such exercise he documents was taking a favorite magazine of his, The
Spectator, and taking notes on articles that appeared there. He would then leave
the notes for a few days and come back to them, trying to reconstruct the original
argument from memory. After finishing, he “compared my Spectator with the
original, discovered some of my faults, and corrected them.” Realizing that his
vocabulary was limited, he developed another strategy. By turning the prose into
verse, he could replace words with synonyms that matched in meter or rhyme.
To improve his sense of the rhetorical flow of an essay, he tried his imitation
approach again, but this time he jumbled up the hints so he would have to
determine the correct order of the sequence of ideas as he wrote again.
Once he had established some of the mechanics of writing, he moved on to the
more difficult task of writing in a style that would persuade. When reading an
English grammar book, he was exposed to the idea of the Socratic method, of
challenging another’s ideas through probing questions rather than direct
contradiction. He then went to work, carefully avoiding “abrupt contradiction
and positive argumentation,” instead focusing on being the “humble inquirer and
doubter.”
Those early efforts produced results. At age sixteen, he wanted to try to get his
work published. Fearing that his elder brother might reject it out of hand,
however, he disguised his penmanship and submitted his essay under the
pseudonym Silence Dogood, purporting to be a widowed woman living in the
countryside. His brother, not knowing the true author, approved and published
the essay, so Franklin returned and wrote more. Although initiated as a ruse to
have his writing considered fairly, Franklin’s practice in adopting other
characters would prove invaluable in his later career. Poor Richard’s Almanack,
for example, was written from the perspective of a simple husband and wife,
Richard and Bridget Saunders, and his political essays such as his “An Edict by
the King of Prussia” similarly made use of his flexibility to adopt imagined
perspectives.
It’s difficult to imagine Franklin having become the household name he is
today without his having first established a mastery of writing. Whether it was
business, science, or statecraft, the unchanging core of what made him
persuasive and great was his ability to write well. What distinguished Franklin
wasn’t merely the amount he wrote or his raw talent but how he practiced. The
way in which he decided to break apart the skill of writing and practice its
elements in isolation enabled him to master writing at a young age and apply it
to the other pursuits for which he would later become famous. Such careful
analysis and deliberate practice forms the basis for the fourth ultralearning
principle: drill.
The Chemistry of Learning
In chemistry, there’s a useful concept known as the rate-determining step. This
occurs when a reaction takes place over multiple steps, with the products of one
reaction becoming the reagents for another. The rate-determining step is the
slowest part of this chain of reactions, forming a bottleneck that ultimately
defines the amount of time needed for the entire reaction to occur. Learning, I’d
like to argue, often works similarly, with certain aspects of the learning problem
forming a bottleneck that controls the speed at which you can become more
proficient overall.
Consider learning mathematics. This is a complex skill that has many different
parts: you need to be able to understand the fundamental concepts, you need to
be able to remember the algorithm for solving a certain type of problem, and you
need to know in what context it applies. Underlying this ability, however, is the
ability to do arithmetic and algebra so as to be able to solve the problems in
ability to do arithmetic and algebra so as to be able to solve the problems in
question. If your arithmetic is weak or your algebra sloppy, you’ll get the wrong
answers even if you’ve mastered the other concepts.
Another rate-determining step could be vocabulary when learning a foreign
language. The number of sentences you can successfully utter depends on how
many words you know. If you know too few, you won’t be able to talk about
very much. If you were able to suddenly inject hundreds of new words into your
mental database, you might drastically expand your fluency even if your
pronunciation, grammar, or other linguistic knowledge remains unchanged.
This is the strategy behind doing drills. By identifying a rate-determining step
in your learning reaction, you can isolate it and work on it specifically. Since it
governs the overall competence you have with that skill, by improving at it you
will improve faster than if you try to practice every aspect of the skill at once.
That was Franklin’s insight that allowed him to rapidly improve his writing: by
identifying components of the overall skill of writing, figuring out which
mattered in his situation, and then coming up with clever ways to emphasize
them in his practice, he could get better more quickly than if he had just spent a
lot of time writing.
Drills and Cognitive Load
Rate-determining steps in learning—where one component of a complex skill
determines your overall level of performance—are a powerful reason to apply
drills. However, they aren’t the only one. Even if there isn’t one isolatable aspect
of the skill that is holding back your performance, it may still be a good idea to
apply drills.
The reason is that when you are practicing a complex skill, your cognitive
resources (attention, memory, effort, etc.) must be spread over many different
aspects of the task. When Franklin was writing, he had to consider not only the
logical content of the argument he was making but word choice and rhetorical
style. This can create a learning trap. In order to improve your performance in
one aspect, you may need to devote so much attention to that one aspect that the
other parts of your performance start to go down. If you can judge yourself only
on how much you improve at the overall task, it can lead to a situation in which
your improvement slows down because you will be getting worse at the overall
task while becoming better at a specific component of it.
Drills resolve this problem by simplifying a skill enough that you can focus
Drills resolve this problem by simplifying a skill enough that you can focus
your cognitive resources on a single aspect. When Franklin focused on
reconstructing the order of an essay he had read previously, he could devote all
his attention to asking what sequence of ideas leads to a good essay rather than
also needing to worry about word usage, grammar, and the content of the
arguments.
Astute readers will probably notice a tension between this principle and the
last. If direct practice involves working on a whole skill nearest to the situation
in which it will eventually be used, drills are a pull in the opposite direction. A
drill takes the direct practice and cuts it apart, so that you are practicing only an
isolated component. How can you resolve this contradiction?
The Direct-Then-Drill Approach
The tension between learning directly and doing drills can be resolved when we
see them as being alternating stages in a larger cycle of learning. The mistake
made in many academic strategies for learning is to ignore the direct context or
abstract it away, in the hope that if enough component skills are developed, they
will eventually transfer. Ultralearners, in contrast, frequently employ what I’ll
call the Direct-Then-Drill Approach.
The first step is to try to practice the skill directly. This means figuring out
where and how the skill will be used and then trying to match that situation as
close as is feasible when practicing. Practice a language by actually speaking it.
Learn programming by writing software. Improve your writing skills by penning
essays. This initial connection and subsequent feedback loop ensure that the
transfer problem won’t occur.
The next step is to analyze the direct skill and try to isolate components that
are either rate-determining steps in your performance or subskills you find
difficult to improve because there are too many other things going on for you to
focus on them. From here you can develop drills and practice those components
separately until you get better at them.
The final step is to go back to direct practice and integrate what you’ve
learned. This has two purposes. The first is that even in well-designed drills,
there are going to be transfer hiccups owing to the fact that what was previously
an isolated skill must be moved to a new and more complex context. Think of
this as being like building the connective tissue to join the muscles you
strengthened separately. The second function of this step is as a check on
strengthened separately. The second function of this step is as a check on
whether your drill was well designed and appropriate. Many attempts to isolate a
drill may end in failure because the drill doesn’t really cut at the heart of what
was difficult in real practice. That’s okay; this feedback is important to help you
minimize wasting time learning things that don’t matter much to your end goals.
The earlier you are in the learning process, the faster this cycle should be.
Cycling between direct practice and drills, even within the same learning
session, is a good idea when you’re just starting out. Later, as you get better at
what you are trying to do and a lot more effort is required to noticeably improve
your overall performance, it’s more acceptable to take longer detours into drills.
As you approach mastery, your time may end up focused mostly on drills as
your knowledge of how the complex skill breaks down into individual
components becomes more refined and accurate and improving any individual
component gets harder and harder.
Tactics for Designing Drills
There are three major problems when applying this principle. The first is
figuring out when and what to drill. You should focus on what aspects of the
skill might be the rate-determining steps in your performance. Which aspect of
the skill, if you improved it, would cause the greatest improvement to your
abilities overall for the least amount of effort? Your accounting skills might be
limited by the fact that your Excel knowledge is superficial, which prevents you
from applying all the things you know to practical situations. Your language
abilities may be held back by having inaccurate pronunciation, even though you
know the right words. Look as well to aspects of a skill that you need to juggle
simultaneously. These may be harder to improve because you can’t devote
enough cognitive resources to improving them. When writing a new article, you
may have to juggle research, storytelling, vocabulary, and many other aspects
simultaneously, making it hard to get a lot better at just one. Determining what
to drill may seem tricky, but it doesn’t have to be. The key is to experiment.
Make a hypothesis about what is holding you back, attack it with some drills,
using the Direct-Then-Drill Approach, and you can quickly get feedback about
whether you’re right.
The second difficulty with this principle is designing the drill to produce
improvement. This is often hard because even if you recognize an aspect of your
performance you’re weak on, it may be tricky to design a drill that trains that
component without artificially removing what makes it difficult in actual
component without artificially removing what makes it difficult in actual
application. Franklin’s drills were uncommon, I believe, because most people,
even recognizing specific deficits in their writing ability, would not have had the
ingenuity to find ways to drill subskills such as ordering arguments persuasively
and emulating a successful writing style.
Finally, doing drills is hard and often uncomfortable. Teasing out the worst
thing about your performance and practicing that in isolation takes guts. It’s
much more pleasant to spend time focusing on things you’re already good at.
Given this natural tendency, let’s look at some good ways to do drills so you can
start applying them yourself.
Drill 1: Time Slicing
The easiest way to create a drill is to isolate a slice in time of a longer sequence
of actions. Musicians often do this kind of training when they identify the
hardest parts of a piece of music and practice each one until it’s perfect before
integrating it back into the context of the entire song or symphony. Athletes
similarly engage in this process when they drill skills that are normally a fraction
of total playing time, such as layups or penalty shots. In the early phase of
learning a new language, I often obsessively repeat a few key phrases, so they
quickly get embedded into my long-term memory. Look for parts of the skill
you’re learning that can be decomposed into specific moments of time that have
heightened difficulty or importance.
Drill 2: Cognitive Components
Sometimes what you’ll want to practice isn’t a slice in time of a larger skill but a
particular cognitive component. When speaking a language, grammar,
pronunciation, and vocabulary occur at all moments, but they form different
cognitive aspects that must be managed simultaneously. The tactic here is to find
a way to drill only one component when, in practice, others would be applied at
the same time. When learning Mandarin Chinese, I would do tone drills that
involved pronouncing pairs of words with different tones and recording myself
speaking. That allowed me to practice producing different tones quickly, without
the distraction of needing to remember what the words meant or how to form
grammatically correct sentences.
Drill 3: The Copycat
A difficulty with drills in many creative skills is that it is often impossible to
practice one aspect without also doing the work of the others. When Franklin
was trying to improve his ability to order arguments logically, for instance, it
wasn’t possible to do so without writing an entire essay. To solve this problem in
your own learning, you can take a page from Franklin: by copying the parts of
the skill you don’t want to drill (either from someone else or your past work),
you can focus exclusively on the component you want to practice. Not only does
this save a lot of time, because you need to repeat only the part you’re drilling, it
also reduces your cognitive burden, meaning you can apply more focus to
getting better at that one aspect. When practicing drawing, I started by drawing
not just from photos but from drawings other people had done. That helped me
focus on the skill of accurately rendering the picture, simplifying the decision
about how to frame the scene and which details to include. For flexible creative
works, editing works you’ve created in the past may have the same effect,
allowing you to selectively improve an aspect of your work without having to
consider the other demands of an original composition.
Drill 4: The Magnifying Glass Method
Suppose you need to create something new and can’t edit or separate out the part
you want to practice. How can you create a drill? The Magnifying Glass Method
is to spend more time on one component of the skill than you would otherwise.
This may reduce your overall performance or increase your input time, but it will
allow you to spend a much higher proportion of your time and cognitive
resources on the subskill you want to master. I applied this method when trying
to improve my ability to do research when writing articles, by spending about
ten times as long on research as I had previously. Although I still had to do all
the other parts of writing the article, by spending much longer on research than I
would normally, I could develop new habits and skills for doing so.
Drill 5: Prerequisite Chaining
One strategy I’ve seen repeatedly from ultralearners is to start with a skill that
they don’t have all the prerequisites for. Then, when they inevitably do poorly,
they go back a step, learn one of the foundational topics, and repeat the exercise.
they go back a step, learn one of the foundational topics, and repeat the exercise.
This practice of starting too hard and learning prerequisites as they are needed
can be frustrating, but it saves a lot of time learning subskills that don’t actually
drive performance much. Eric Barone, for instance, started his pixel art
experiments simply by making them. When he struggled with certain aspects,
such as colors, he went back, learned color theory, and repeated his work. Benny
Lewis has a similar habit of starting with speaking from a phrase book and only
later learning the grammar that explains how the phrases function.
Mindful Drilling
To many, the idea of drilling may seem to be a push in the wrong direction.
We’ve all spent time doing homework designed to drill into us facts and
procedures that turned out to be a total waste of time. That was often because we
didn’t know the reasons behind what we were practicing or how it fit into a
broader context. Drilling problems without context is mind-numbing. However,
once you’ve identified that it’s the bottleneck preventing you from going further,
they become instilled with new purpose. In ultralearning, which is directed by
the student, not an external source, drills take on a new light. Instead of being
forced to do them for unknown purposes, it is now up to you to find a way to
enhance the learning process by accelerating learning on the specific things that
you find most difficult. In this sense, drills take on a very different flavor in
ultralearning as opposed to traditional learning. Far from being meaningless
drudgery, carefully designed drills elicit creativity and imagination as you strive
to solve a more complex learning challenge by breaking it into specific parts.
Drills are hard to do, which is why many of us would rather avoid them.
When we do engage in drills, it’s often in subjects where we feel competent and
comfortable. Drills require the learner not only to think deeply about what is
being learned but also figure out what is most difficult and attack that weakness
directly rather than focus on what is the most fun or what has already been
mastered. This requires strong motivation and a comfort with learning
aggressively. Franklin, in his Autobiography, remarked about the lengths he
went to so he could dedicate himself to his writing drills: “My time for these
exercises and for reading was at night, after work or before it began in the
morning.” Despite the prominence writing would play in his life, Franklin still
had to work long hours under his taskmaster brother in the print shop, diligently
improving his craft in what little leisure time he had. Eric Barone similarly
repeated his pixel art dozens of times, going back to master prerequisite concepts
and theory until he got it perfect.
The difficulty and usefulness of drills repeat a pattern that will recur
throughout the ultralearning principles: that something mentally strenuous
provides a greater benefit to learning than something easy. Nowhere is this
pattern more clear than in the next principle, retrieval, where difficulty itself may
be the key to more effective learning.
Chapter VIII
Principle 5
Retrieval
Test to Learn
It pays better to wait and recollect by an effort from within, than to look at the book
again.
—William James, psychologist
In the spring of 1913, the mathematician G. H. Hardy received a letter that
would forever define the course of his life. Sent by an accounting clerk working
for the Port Trust Office of Madras in India, the letter contained a humble note
of introduction along with some startling assertions. The author claimed that he
had found theorems for problems that the best mathematical minds of the time
had yet to solve. What’s more, he claimed that he had “no University education”
and had derived these results from his own solitary investigations.
1
Receiving letters from amateur crackpots who claimed to have solutions to
famous problems was a common occurrence for someone of Hardy’s stature in
mathematics, so at first he simply dismissed the letter as being more of the same.
Still, flipping through the several pages of notes attached to the letter, the
equations wouldn’t leave his mind. When he found himself thinking about them
hours later, he brought the letter to the attention of his colleague John
Littlewood. As the two of them toyed at trying to prove the strange assertions,
Littlewood. As the two of them toyed at trying to prove the strange assertions,
they found that some of them they were able to prove with great effort, while
others remained, in Hardy’s words, “scarcely possible to believe.” Maybe, Hardy
thought, this wasn’t a letter from a crackpot but something rather different.
The formulas written were so bizarre and alien that Hardy remarked, “They
must be true because, if they were not true, no one would have had the
imagination to invent them.” What he only vaguely understood that day was that
he had just had his first introduction to one of the most brilliant and bizarre
mathematicians of all time, Srinivasa Ramanujan.
Ramanujan’s Genius
Before writing his letter to Hardy, which changed the course of mathematical
history, Ramanujan was a poor, pudgy south Indian boy with a special love of
equations. More than anything else, he loved math. In fact, his love of math
often got him into difficulties. His unwillingness to study other subjects flunked
him out of university. Equations were all he cared about. In his spare time and
during stretches of unemployment, he would sit for hours on the bench in front
of his family home, slate in hand, playing with formulas. Sometimes he would
stay up so late that his mother would need to put food into his hand so he would
eat.
As he was thousands of miles away from the center of mathematics of his day,
access to high-quality textbooks was quite a challenge for Ramanujan. One
resource he did encounter and mined extensively was a volume by George
Shoobridge Carr called A Synopsis of Elementary Results in Pure and Applied
Mathematics. Carr himself was hardly a towering figure of mathematical genius.
The book, intended as a guide for students, included large lists of various
theorems from different fields of mathematics, usually without explanation or
proof. However, even without having proofs or explanations available, Carr’s
book became a powerful resource in the hands of someone smart and obsessed
like Ramanujan. For instead of simply copying and memorizing how certain
theorems were derived, he had to figure them out for himself.
Though many commentators of the time, including Hardy, argued that
Ramanujan’s impoverished upbringing and late access to the cutting edge of
mathematics likely did irreparable harm to his genius, modern psychological
experiments may offer an alternative perspective, for when Ramanujan dealt
only with Carr’s extensive list of theorems using his own quirky obsession with
mathematical formulas, he was unwittingly practicing one of the most powerful
mathematical formulas, he was unwittingly practicing one of the most powerful
methods known to build a deep understanding.
The Testing Effect
Imagine you’re a student preparing for an exam. You have three choices about
how you can allocate your limited studying time. First, you can review the
material. You can look over your notes and book and study everything until
you’re sure you’ll remember it. Second, you can test yourself. You can keep the
book shut and try to remember what was in it. Finally, you can create a concept
map. You can write out the main concepts in a diagram, showing how they’re
organized and related to other items you need to study. If you can pick only one,
which one should you choose to do best on the final exam?
This is essentially the question posed by the psychologists Jeffrey Karpicke
and Janell Blunt in one study examining students’ choice of learning strategy.
2
In the study, students were divided into four groups, each given the same amount
of time but told to use different study strategies: reviewing the text a single time,
reviewing it repeatedly, free recall, and concept mapping. In each group,
students were asked to predict their score on the upcoming test. Those who did
repeated reviewing predicted that they’d score the best, followed by the singlestudy and concept-mapping groups. Those who practiced free recall (trying to
remember as much as they could without looking in the book) predicted the
worst for their final performance.
The actual results, however, weren’t even close. Testing yourself—trying to
retrieve information without looking at the text—clearly outperformed all other
conditions. On questions based directly on the content of the text, those who
practiced free recall remembered almost 50 percent more than the other groups.
How could students, who have spent years getting firsthand experience about
what matters to learning, be so misguided about what actually produces results?
One might be tempted to argue that this benefit of self-testing is an artifact of
the way success is measured. The principle of directness asserts that transfer is
difficult. Since self-testing and actual testing are most similar, perhaps it is this
similarity that allows this method to work better. Had the method of evaluation
differed, it might be reasonable to suspect that review or concept mapping might
come out on top. Interestingly, in another experiment, Karpicke and Blunt
showed that this wasn’t the explanation, either. In this experiment the final test
was to produce a concept map. Despite the overwhelming similarity to the
was to produce a concept map. Despite the overwhelming similarity to the
evaluation task, free recall still did better than using concept mapping to study.
Another possible explanation for why self-testing works is feedback. When
you review something passively, you don’t get any feedback about what you
know and don’t know. Since tests usually come with feedback, that might
explain why students who practiced self-testing beat the concept mappers or
passive reviewers. Though it is true that feedback is valuable, once again,
retrieval doesn’t simply reduce down to getting more feedback. In the
experiments mentioned, students were asked to do free recall but weren’t
provided any feedback about items they missed or got wrong. The act of trying
to summon up knowledge from memory is a powerful learning tool on its own,
beyond its connection to direct practice or feedback.
This new perspective on learning shows how Carr’s book, with its lists of
proofs without solutions, could have become, in the hands of someone
sufficiently motivated to master them, an incredible tool for becoming brilliant at
math. Without the answers at hand, Ramanujan was forced to invent his own
solutions to the problems, retrieving information from his mind rather than
reviewing it in a book.
The Paradox of Studying
If retrieval practice—trying to recall facts and concepts from memory—is so
much better for learning, why don’t students realize it? Why do many prefer to
stick to concept mapping or the even less effective passive review, when simply
closing the book and trying to recall as much as possible would help them so
much more?
Karpicke’s research points to a possible explanation: Human beings don’t
have the ability to know with certainty how well they’ve learned something.
Instead, we need to rely on clues from our experience of studying to give us a
feeling about how well we’re doing. These so-called judgments of learning
(JOLs) are based, in part, on how fluently we can process something. If the
learning task feels easy and smooth, we are more likely to believe we’ve learned
it. If the task feels like a struggle, we’ll feel we haven’t learned it yet.
Immediately after spending some time studying, these JOLs may even be
accurate. Minutes after studying something using a strategy of passive review,
students perform better than they would if they had practiced retrieval.
3 The
feeling that you’re learning more when you’re reading rather than trying to recall
with a closed book isn’t inaccurate. The problem comes after. Test again days
later, and retrieval practice beats passive review by a mile. What helped in the
immediate time after studying turns out not to create the long-term memory
needed for actual learning to take place.
Another explanation for why students opt for low-efficiency review instead of
retrieval is that they don’t feel they know the material well enough to test
themselves on it. In another experiment, Karpicke had students choose a strategy
for learning. Inevitably, students who were performing more weakly elected to
review the material first, waiting until they were “ready” to start practice
testing.
4
If through experimental intervention, however, they were forced to
practice retrieval earlier, they learned more. Whether you are ready or not,
retrieval practice works better. Especially if you combine retrieval with the
ability to look up the answers, retrieval practice is a much better form of
studying than the ones most students apply.
Is Difficulty Desirable?
What makes practicing retrieval so much better than review? One answer comes
from the psychologist R. A. Bjork’s concept of desirable difficulty.
5 More
difficult retrieval leads to better learning, provided the act of retrieval is itself
successful. Free recall tests, in which students need to recall as much as they can
remember without prompting, tend to result in better retention than cued recall
tests, in which students are given hints about what they need to remember. Cued
recall tests, in turn, are better than recognition tests, such as multiple-choice
answers, where the correct answer needs to be recognized but not generated.
Giving someone a test immediately after they learn something improves
retention less than giving them a slight delay, long enough so that answers aren’t
in mind when they need them. Difficulty, far from being an obstacle to making
retrieval work, may be part of the reason it does so.
The idea of desirable difficulties in retrieval makes a potent case for the
ultralearning strategy. Low-intensity learning strategies typically involve either
less or easier retrieval. Pushing difficulty higher and opting for testing oneself
well before you are “ready” is more efficient. One can think back to Benny
Lewis’s strategy of speaking a new language from the first day. Though this
approach is high in difficulty, research suggests why it might be more useful
than easier forms of classroom study. Placing himself in a more difficult context
than easier forms of classroom study. Placing himself in a more difficult context
means that every time Lewis needs to recall a word or phrase, it will be
remembered more strongly than when doing the same act of retrieval in a
classroom setting and much better than when simply looking over a list of words
and phrases.
Difficulty can become undesirable if it gets so hard that retrieval becomes
impossible. Delaying the first test of a newly learned fact has some benefits over
testing immediately.
6 However, if you delay the test too long, the information
may be forgotten entirely.
7 The idea, therefore, is to find the right midpoint: far
enough away to make whatever is retrieved remembered deeply, not so far away
that you’ve forgotten everything. Although waiting too long before you test
yourself may have disadvantages, increasing difficulty by giving yourself fewer
clues and prompts are likely helpful, provided that you can get some feedback on
them later.
Should You Take the Final Exam Before the Class Even
Begins?
The standard way of viewing tests is that they work to evaluate the knowledge
you have learned elsewhere—through reading or listening to lectures. The
concept of retrieval flips this view on its head, suggesting that the act of taking a
test not only is a source of learning but results in more learning than a similar
amount of time spent in review. However, this still fits within the conventional
idea of knowledge being first acquired, and then strengthened or tested later.
An interesting observation from retrieval research, known as the forwardtesting effect, shows that retrieval not only helps enhance what you’ve learned
previously but can even help prepare you to learn better.
8 Regular testing of
previously studied information can make it easier to learn new information. This
means that retrieval works to enhance future learning, even when there is
nothing to retrieve yet!
A variety of mechanisms has been proposed for explaining why this forward
testing effect exists. Some researchers argue that it may be that trying to find
knowledge that hasn’t been learned yet—say, by trying to solve a problem you
haven’t learned the answer to yet—nonetheless helps reinforce search strategies
that are put to use once the knowledge is encountered later. An analogy here is
that trying to retrieve an answer that doesn’t yet exist in your mind is like laying
that trying to retrieve an answer that doesn’t yet exist in your mind is like laying
down a road leading to a building that hasn’t been constructed yet. The
destination doesn’t exist, but the path to get to where it will be, once constructed,
is developed regardless. Other researchers argue that the mechanism might be
one of attention. By confronting a problem you don’t yet know how to answer,
your mind automatically adjusts its attentional resources to spot information that
looks like a solution when you learn it later. Whatever the exact mechanism is,
the reality of the forward-testing effect implies that practicing retrieval might not
only benefit from starting earlier than one is “ready” but even before you have
the possibility of answering correctly.
What Should Be Retrieved?
The research is clear: if you need to recall something later, you’re best off
practicing retrieving it. However, this neglects an important question: What
kinds of things should you invest the time in to remember in the first place?
Retrieval may take less time than review to get the same learning impact, but not
learning something at all is faster still. This is an important practical question.
Nobody has time to master everything. During the MIT Challenge, I covered a
lot of different ideas. Some were directly relevant to the kind of programming I
wanted to do when I was done, so making sure I retained those ideas was a
priority. Others were interesting, but since I had no plans to use them
immediately, I put more effort into practicing retrieving the underlying concepts
than doing technical calculations. One class I did, for instance, was Modal
Logic. As I have no plans to be a logician, I can honestly say, eight years later,
that I couldn’t prove theorems in modal logic today. However, I can tell you
what modal logic is for and when it is used, so if a situation arises in which the
techniques I learned in that class might be useful, I’d have a much better time
spotting it.* There will always be some things you choose to master and others
you satisfy yourself with knowing you can look up if you need to.
One way to answer this question is simply to do direct practice. Directness
sidesteps this question by forcing you to retrieve the things that come up often in
the course of using the skill. If you’re learning a language and need to recall a
word, you’ll practice it. If you never need a word, you won’t memorize it. The
advantage of this strategy is that it automatically leads you to learn the things
with the highest frequency. Things that are rarely used or that are easier to look
up than to memorize won’t be retrieved. These tend to be the things that don’t
up than to memorize won’t be retrieved. These tend to be the things that don’t
matter so much.
The problem with relying on direct practice exclusively is that knowledge that
isn’t in your head can’t be used to help you solve problems. For instance, a
programmer may realize a need to use a certain function to solve a problem but
forgets how to write it out. Needing to look up the syntax might slow her down,
but she will still be able to solve the problem. However, if you don’t have
enough knowledge stored to recognize when you can use a function to solve
your problem, no looking up can help you. Consider that over the last twenty
years, the amount of knowledge easily accessible from a quick online search has
exploded. Nearly any fact or concept is now available on demand to anyone with
a smartphone. Yet despite this incredible advance, it is not as if the average
person is thousands as times as smart as people were was a generation ago.
Being able to look things up is certainly an advantage, but without a certain
amount of knowledge inside your head, it doesn’t help you solve hard problems.
Direct practice alone can fail to encourage enough retrieval by omitting
knowledge that can help you solve a problem but isn’t strictly necessary to do
so. Consider our programmer who has two different ways to solve her problem,
A and B. Option A is much more effective, but B will also get the job done. Now
suppose that she knows only about option B. She’ll continue to use the way she
knows to solve the problem, even though it is less effective. Here, our fledgling
programmer might read about option A on a blog somewhere. But since simply
reading is much less effective than repeated retrieval practice, chances are that
she’ll forget about it when it comes time to apply the technique. This may sound
abstract, but I’d argue that this is quite common with programmers, and often the
thing separating mediocre programmers from great ones isn’t the range of
problems they can solve but that the latter often know dozens of ways to solve
problems and can select the best one for each situation. This kind of breadth
requires a certain amount of passive exposure, which in turn benefits from
retrieval practice.
How to Practice Retrieval
Retrieval works, but it isn’t always easy. Not only is the effort itself an obstacle,
but sometimes it’s not clear exactly how to do it. Passive review may not be very
efficient, but at least it’s straightforward: you open your book and reread
material until you retain it. Most books and resources don’t have a handy list of
questions at the end to test you to see if you remember what they contain. To
questions at the end to test you to see if you remember what they contain. To
help with that, below are some useful methods that can be used to apply retrieval
to almost any subject.
Tactic 1: Flash Cards
Flash cards are an amazingly simple, yet effective, way to learn paired
associations between questions and answers. The old way of creating paper flash
cards to drill yourself is powerful, but it has largely been superseded by spacedrepetition systems, as I’ll discuss in Principle 7. These software algorithms can
handle tens of thousands of “cards” and also organize a review schedule so you
can manage them.
The major drawback of flash cards is that they work really well for a specific
type of retrieval—when there’s a pairing between a specific cue and a particular
response. For some forms of knowledge, for example memorizing foreignlanguage vocabulary, this works perfectly. Similarly, maps, anatomical
diagrams, definitions, and equations can often be memorized via flash cards.
However, when the situation in which you need to remember the information is
highly variable, this kind of practice can have drawbacks. Programmers can
memorize syntax via flash cards, but concepts that need to be applied in real
programs often don’t fit the cue-response framework that flash cards demand.
Tactic 2: Free Recall
A simple tactic for applying retrieval is, after reading a section from a book or
sitting through a lecture, to try to write down everything you can remember on a
blank piece of paper. Free recall like this is often very difficult, and there will be
many things missed, even if you just finished reading the text in question.
However, this difficulty is also a good reason why this practice is helpful. By
forcing yourself to recall the main points and arguments, you’ll be able to
remember them better later. While doing research for this book, for instance, I
would often print out journal articles and put them in a binder with a few blank
sheets of paper after each of them. After I had finished reading, I’d do a quick
free recall exercise to make sure I would retain the important details when it
came time for writing.
Tactic 3: The Question-Book Method
Tactic 3: The Question-Book Method
Most students take notes by copying the main points as they encounter them.
However, another strategy for taking notes is to rephrase what you’ve recorded
as questions to be answered later. Instead of writing that the Magna Carta was
signed in 1215, you could instead write the question “When was the Magna
Carta signed?” with a reference to where to find the answer in case you forget.
By taking notes as questions instead of answers, you generate the material to
practice retrieval on later.
One mistake I’ve made in applying this technique is to focus on the wrong
kinds of things to ask questions about. I tried applying this method to a book on
computational neuroscience, and I ended up asking myself all sorts of detailed
questions such as what was the firing rate of certain neuronal circuits or who
proposed a specific theory. That wasn’t intentional but rather a by-product of
lazily restating the factual content in the book as questions. What’s harder and
more useful is to restate the big idea of a chapter or section as a question. Since
this is often implicit, it requires some deeper thinking and not just adding a
question mark to some notes you copied verbatim. One rule I’ve found helpful
for this is to restrict myself to one question per section of a text, thus forcing
myself to acknowledge and rephrase the main point rather than zoom in on a
detail that will be largely irrelevant later.
Tactic 4: Self-Generated Challenges
The above tactics work best with retrieval of simple information, such as facts or
summaries of broad ideas you might encounter in a book or lecture. However, if
you’re trying to practice a skill, not merely remember information, they might
not be enough. For a programmer, it’s not enough to know what an algorithm
means, but be able to write it in code. In this case, as you go through your
passive material, you can create challenges for yourself to solve later. You may
encounter a new technique and then write a note to demonstrate that technique in
an actual example. Creating a list of such challenges can serve as a prompt for
mastering that information later in practice and can expand your library of tools
that you are able to actually apply.
Tactic 5: Closed-Book Learning
Nearly any learning activity can become an opportunity for retrieval if you cut
off the ability to search for hints. Concept mapping, the strategy that didn’t work
particularly well for students in Karpicke and Blunt’s experiments, could be
beefed up considerably by preventing yourself from looking at the book when
generating your concept map. I suspect that had this been done in the original
experiment, students using this form of closed-book concept mapping would
likely have done better on the eventual test that relied on creating a concept map.
Any practice, whether direct or a drill, can be cut off from the ability to look
things up. By preventing yourself from consulting the source, the information
becomes knowledge stored inside your head instead of inside a reference
manual.
Revisiting Ramanujan
Ramanujan was smart, there’s no denying it. However, his genius was aided
immeasurably by two hallmarks of the ultralearner’s tool kit: obsessive intensity
and retrieval practice. As he worked on his slate from morning to night, trying to
figure out Carr’s sparsely written list of theorems was incredibly hard work. But
it also created the desirable difficulties that allowed him to build a huge mental
library of tools and tricks that would assist him in his later mathematical efforts.
Retrieval played an important role in Ramanujan’s mathematical upbringing,
but he is hardly the only one to take advantage of the tactic. In nearly every
biography of great geniuses and contemporary ultralearners I have encountered,
some form of retrieval practice is mentioned. Benjamin Franklin practiced his
writing by reconstructing essays from memory. Mary Somerville worked
through problems mentally when no candle was available for night reading.
Roger Craig practiced trivia questions without looking at the answers. Retrieval
is not a sufficient tool to create genius, but it may be a necessary one.
Trying to produce the answer rather than merely reviewing it is only half of a
bigger cycle, however. To make retrieval really effective, it helps to know
whether the answer you dredged up from your mind was correct. Just as we
often avoid testing ourselves until we’re ready because struggling with a test is
uncomfortable, we often avoid seeking information about our skill level until we
think it will be favorable. Being able to process that information effectively,
hearing the message it contains loud and clear, isn’t always easy. Yet this is also
why it is so important. This brings us to the next principle of ultralearning:
feedback.
feedback.
Chapter IX
Principle 6
Feedback
Don’t Dodge the Punches
Everybody has a plan until they get punched in the mouth.
—Mike Tyson
From a narrow staircase in the back, Chris Rock enters the stage just as his
name is being announced. With sold-out shows and HBO specials, Rock is no
neophyte to stand-up comedy. His performances feel like a rock concert. With an
energetic and punctuated delivery, he’s known for repeating the key phrase of a
joke like the chorus of a song, the rhythm of it so precise that you get the feeling
he would be able to make anything funny. And that’s exactly the problem. When
everything you do is funny, how do you know what really makes a joke good?
Far from the packed concert halls and jubilant crowds, Rock walks to the mic
on the modest brick-backed stage at the Comedy Cellar in Greenwich Village,
New York City. In his hand are scraps of cards on which he has scribbled bits of
phrases, a trick for working out new material he learned from his grandfather, a
cab driver who preached on weekends. Instead of his signature aggressive style,
he slumps against the back wall. This is his laboratory, and he’s going to
perform comedy with the precision of an experiment.
“It’s not going to be that good,” Rock warns the crowd, who are stunned at his
unannounced arrival on the small comedy stage. “Not at these prices,” he adds,
joking “At these prices, I could leave right now!” He envisions the reviews:
“Chris came out and he left. It was good! He didn’t tell any jokes—but it was
good!” Notes in hand, Rock warns the audience playfully that this isn’t going to
be a typical Chris Rock performance. Instead, he wants to work out new material
under controlled conditions. “They’ll give you about six minutes because you’re
famous,” he explains. “. . . then you’re back to square one.” He wants to know
what’s funny, when he’s not trying to be funny.
1
Rock’s method is not unique. The Comedy Cellar is famous for big-name
drop-ins: Dave Chappelle, Jon Stewart, and Amy Schumer are just a few
comedians who have tested out their material in front of small crowds here
before performing it on prime-time specials and in concert-scale gigs. Why
perform at a small club when you can easily draw large crowds and thousands of
dollars from a huge performance? Why show up unannounced and then
deliberately undersell your own comedic abilities? What Rock and these other
famous comedians recognize is the importance of the sixth principle of
ultralearning: feedback.
The Power of Information
Feedback is one of the most consistent aspects of the strategy ultralearners use.
From the simple feedback of Roger Craig testing himself on Jeopardy! clues
without knowing the answer to the uncomfortable feedback of Benny Lewis’s
approach of walking up to strangers to speak a language he only started learning
the day prior, getting feedback was one of the most common tactics of the
ultralearners I encountered. What often separated the ultralearning strategy from
more conventional approaches was the immediacy, accuracy, and intensity of the
feedback being provided. Tristan de Montebello could have taken the normal
route of carefully preparing his script and then delivering a speech once every
month or two, as is the case for most Toastmasters. Instead he dove straight in,
speaking several times per week, jumping among different clubs to gather
different perspectives on his performance. This deep dive into feedback was
uncomfortable, but the rapid immersion also desensitized him to a lot of the
anxiety that being onstage can create.
Feedback features prominently in the research on deliberate practice, a
scientific theory of the acquisition of expertise initiated by K. Anders Ericsson
and other psychologists. In his studies, Ericsson has found that the ability to gain
immediate feedback on one’s performance is an essential ingredient in reaching
expert levels of performance. No feedback, and the result is often stagnation—
long periods of time when you continue to use a skill but don’t get any better at
it. Sometimes the lack of feedback can even result in declining abilities. Many
medical practitioners get worse with more experience as their accumulated
knowledge from medical school begins to fade and the accuracy of their
diagnoses is not given the rapid feedback that would normally promote further
learning.
2
Can Feedback Backfire?
The importance of feedback probably isn’t too surprising; we all intuitively
sense how getting information about what we’re doing right and wrong can
accelerate learning. More interestingly, the research on feedback shows that
more isn’t always better. Crucially, what matters is the type of feedback being
given.
In a large meta-analysis, Avraham Kluger and Angelo DeNisi looked at
hundreds of studies on the impact of providing feedback for learning.
3 Though
the overall effect of feedback was positive, it’s important to note that in over 38
percent of cases, feedback actually had a negative impact. This leads to a
confusing situation. On the one hand, feedback is essential for expert attainment,
as demonstrated by the scientific studies of deliberate practice. Feedback also
figures prominently in ultralearning projects, and it’s difficult to imagine their
being successful if their sources of feedback had been turned off. At the same
time, a review of the evidence doesn’t paint the picture of feedback being
universally positive. What’s the explanation?
Kluger and DeNisi argue that the discrepancy is in the type of feedback that is
given. Feedback works well when it provides useful information that can guide
future learning. If feedback tells you what you’re doing wrong or how to fix it, it
can be a potent tool. But feedback often backfires when it is aimed at a person’s
ego. Praise, a common type of feedback that teachers often use (and students
enjoy), is usually harmful to further learning. When feedback steers into
evaluations of you as an individual (e.g., “You’re so smart!” or “You’re lazy”),
it usually has a negative impact on learning. Further, even feedback that includes
useful information needs to be correctly processed as a motivator and tool for
useful information needs to be correctly processed as a motivator and tool for
learning. Kluger and DeNisi noted that some of the studies that showed a
negative impact of feedback occurred because the subjects themselves chose not
to use the feedback constructively. They may have rejected the feedback,
lowered the standards they expect from themselves, or given up on the learning
task altogether. The researchers note that who is giving the feedback can matter,
as feedback coming from a peer or teacher has important social dynamics
beyond mere information on how to improve one’s abilities.
I find two things interesting about this research. First, it is clear that although
informative feedback is beneficial, it can backfire if it is processed
inappropriately or if it fails to provide useful information. This means that when
seeking feedback, the ultralearner needs to be on guard for two possibilities. The
first is overreacting to feedback (both positive and negative) that doesn’t offer
specific information that leads to improvement. Ultralearners need to be
sensitive to what feedback is actually useful and tune out the rest. This is why,
although all the ultralearners I met employed feedback, they didn’t act on every
piece of possible feedback. Eric Barone, for instance, did not attend to every
comment and critique on early drafts of his game. In many cases he ignored
them, when the feedback conflicted with his vision. Second, when it is
incorrectly applied, feedback can have a negative impact on motivation. Not
only can overly negative feedback lower your motivation, but so can overly
positive feedback. Ultralearners must balance both concerns, pushing for the
right level of feedback for their current stage of learning. Though we all know
(and instinctively avoid) harsh and unhelpful criticism, the research also
supports Rock’s strategy of disregarding the positive feedback that his celebrity
automatically generates.
The second interesting point about this research is that it explains why
feedback-seeking efforts are often underused and thus remain a potent source of
comparative advantage for ultralearners. Feedback is uncomfortable. It can be
harsh and discouraging, and it doesn’t always feel nice. Standing up on a stage in
a comedy club to deliver jokes is probably one of the best ways to get better at
stand-up comedy. But the act itself can be terrifying, as an awkward silence cuts
deep. Similarly, speaking immediately in a new language can be painful, as the
sense of your ability to communicate goes down precipitously from when you
use your native tongue.
Fear of feedback often feels more uncomfortable than experiencing the
feedback itself. As a result, it is not so much negative feedback on its own that
can impede progress but the fear of hearing criticism that causes us to shut
can impede progress but the fear of hearing criticism that causes us to shut
down. Sometimes the best action is just to dive straight into the hardest
environment, since even if the feedback is very negative initially, it can reduce
your fears of getting started on a project and allow you to adjust later if it proves
too harsh to be helpful.
All of these acts require self-confidence, resolve, and persistence, which is
why many self-directed learning efforts ignore seeking the aggressive feedback
that could generate faster results. Instead of going to the source, taking feedback
directly, and using that information to learn quickly, people often choose to
dodge the punches and avoid a potentially huge source of learning. Ultralearners
acquire skills quickly because they seek aggressive feedback when others opt for
practice that includes weaker forms of feedback or no feedback at all.
What Kind of Feedback Do You Need?
Feedback shows up in many different forms for different types of learning
projects. Getting good at stand-up comedy and learning to write computer
programs involve very different kinds of feedback. Learning higher math and
learning languages are going to use feedback in different ways. The
opportunities for seeking better feedback will vary depending on what you’re
trying to learn. Rather than try to spell out exactly what feedback you need for
your learning project, I think it’s important to consider different types of
feedback, along with how each one can be used and cultivated. By knowing
what kind of feedback you’re getting, you can make sure to use it best, while
also recognizing its limitations. In particular, I want to consider three types of
feedback: outcome feedback, informational feedback, and corrective feedback.
Outcome feedback is the most common and in many situations the only type of
feedback available. Informational feedback is also fairly common, and it’s
important to recognize when you can split apart outcomes to get feedback on
parts of what you’re learning and when feedback only on holistic outcomes is
possible. Corrective feedback is the toughest to find but when employed well
can accelerate learning the most.
Outcome Feedback: Are You Doing It Wrong?
The first type of feedback, and the least granular, is outcome feedback. This tells
you something about how well you’re doing overall but offers no ideas as to
you something about how well you’re doing overall but offers no ideas as to
what you’re doing better or worse. This kind of feedback can come in the form
of a grade—pass/fail, A, B, or C—or it can come in the form of an aggregate
feedback to many decisions you’re making simultaneously. The applause Tristan
de Montebello received (or the crickets he heard) after a speech is an example of
outcome feedback. It could tell him if he was getting better or worse, but it
couldn’t really say why or how to fix it. Every entrepreneur experiences this
kind of feedback when a new product hits the market. It may sell wildly well or
abysmally, but that feedback comes in bulk, not directly decomposable into the
various aspects of the product. Did the product cost too much? Was the
marketing message not clear enough? Was the packaging unappealing?
Customer reviews and comments can provide clues, but ultimately the success or
failure of any new product is a complex bundle of factors.
This type of feedback is often the easiest to get, and research shows that even
getting this feedback, which lacks a specific message about what you need to
improve, can be helpful. In one study, feedback for a task involving visual acuity
facilitated learning, even when it was delivered in blocks that were too large to
get any meaningful information about which responses were correct and which
were incorrect.
4 Many projects that wholly lack feedback can easily be changed
to get this broad-scale feedback. Eric Barone, for instance, provided a
development blog to publish work on his game and solicit feedback from early
drafts. It couldn’t provide him with detailed information about what exactly to
improve and change, but his simply being immersed in an environment that
provided feedback at all was helpful.
Outcome feedback can improve how you learn through a few different
mechanisms. One is by providing you with a motivational benchmark against
your goal. If your goal is to reach a certain quality of feedback, this feedback can
give you updates on your progress. Another is that it can show you the relative
merits of different methods you’re trying. When you are progressing rapidly,
you can stick to those learning methods and approaches. When progress stalls,
you can see what you might be able to change in your current approach.
Although outcome feedback isn’t complete, it is often the only kind available
and can still have a potent impact on your learning rate.
Informational Feedback: What Are You Doing Wrong?
The next type of feedback is informational feedback. This feedback tells you
what you’re doing wrong, but it doesn’t necessarily tell you how to fix it.
what you’re doing wrong, but it doesn’t necessarily tell you how to fix it.
Speaking a foreign language with a native speaker who doesn’t share a language
with you is an exercise in informational feedback. That person’s confused stare
when you misuse a word won’t tell you what the correct word is, but it will tell
you that you’re getting it wrong. Tristan de Montebello, in addition to the overall
assessment of his performance by audience members at the end of a speech, can
also get live informational feedback about how it’s going moment to moment.
Did that joke work? Is my story boring them? This is something you can spot in
the distracted glances or background chatter throughout your speech. Rock’s
stand-up experiment is also a type of informational feedback. He can tell when a
certain joke lands or doesn’t, based on the reaction of the audience. However,
they can’t tell him what to do to make it funnier—he’s the comedian, not them.
This kind of feedback is easy to obtain when you can get real-time access to a
feedback source. A computer programmer who gets error messages when her
programs don’t compile properly may not have enough knowledge to understand
what she’s doing wrong. But as errors increase or diminish, depending on what
she does, she can use that signal to fix her problems. Self-provided feedback is
also ubiquitous, and in some pursuits it can be almost as good as feedback from
others. When painting a picture, you can simply look at it and get a sense of
whether your brushstrokes are adding to or detracting from the image you want
to convey. Because this kind of feedback often comes from direct interaction
with the environment, it often pairs well with the third principle, directness.
Corrective Feedback: How Can You Fix What You’re
Doing Wrong?
The best kind of feedback to get is corrective feedback. This is the feedback that
shows you not only what you’re doing wrong but how to fix it. This kind of
feedback is often available only through a coach, mentor, or teacher. However,
sometimes it can be provided automatically if you are using the right study
materials. During the MIT Challenge, I did most of my practice by going back
and forth between assignments and their solutions, so that when I finished a
problem, I was shown not only whether I had gotten it right or wrong but exactly
how my answer differed from the correct one. Similarly, flash cards and other
forms of active recall provide corrective feedback by showing you the answer to
a question after you make your guess.
The educators Maria Araceli Ruiz-Primo and Susan M. Brookhart argue, “The
best feedback is informative and usable by the student(s) who receive it. Optimal
feedback indicates the difference between the current state and the desired
learning state AND helps students to take a step to improve their learning.”
5
The main challenge of this kind of feedback is that it typically requires access
to a teacher, expert, or mentor who can pinpoint your mistakes and correct them
for you. However, sometimes the added edge of having corrective over merely
informational feedback can be worth the effort needed to find such people.
Tristan de Montebello worked with Michael Gendler to help him with his public
speaking performance, and that helped him spot subtle weaknesses in his
presentations that would have gone unnoticed by himself or by a less
experienced audience member giving broader feedback.
This type of feedback trumps outcome feedback, which can’t indicate what
needs improving, and informational feedback, which can indicate what to
improve but not how. However, it can also be unreliable. Tristan de Montebello
would often get conflicting advice after delivering a speech; some audience
members would tell him to slow down, while others said to speed up. This can
also be a situation in which paying for a tutor can be useful, because that person
can spot the exact nature of your mistake and correct it with less struggle on
your part. The self-directed nature of ultralearning shouldn’t convince you that
learning is best done as an entirely solitary pursuit.
Further Notes on Types of Feedback
A few things are worth noting here. First, you need to be careful when trying to
“upgrade” feedback from a weaker form to a stronger form if it’s not actually
possible. To switch from outcome feedback to informational feedback, you need
to be able to elicit feedback on a per element basis of what you’re doing. If
instead the feedback is being provided as a holistic assessment of everything
you’re doing, trying to turn it into informational feedback can backfire. Game
designers know to watch out for this, because asking play testers what they don’t
like about a game can often return spurious results: for example, they don’t like
the color of the character or the background music. The truth is, the players are
evaluating the game holistically, so they often can’t offer this kind of feedback.
If their responses come from using it as a whole, not from each aspect
individually, asking for greater specificity may lead to guesses from those giving
feedback.
feedback.
Similarly, corrective feedback requires a “correct” answer or the response of a
recognized expert. If there is no expert or a single correct approach, trying to
turn informational feedback into corrective feedback can work against you when
the wrong change is suggested as an improvement. De Montebello noted to me
that the advice most people gave him wasn’t terribly useful, but the consistency
of it was. If his speech elicited wildly different reactions each time, he knew
there was still a lot of work to do. When the speech started to get much more
consistent comments, he knew he was onto something. This illustrates that
ultralearning isn’t simply about maximizing feedback but also knowing when to
selectively ignore elements of it to extract the useful information. Understanding
the merits of these different types of feedback, as well as the preconditions that
make them possible, is a big part of choosing the right strategy for an
ultralearning project.
How Quick Should Feedback Be?
An interesting question in the research on feedback is how quick it should be.
Should you get immediate information about your mistakes or wait some period
of time? In general, research has pointed to immediate feedback being superior
in settings outside of the laboratory. James A. Kulik and Chen-Lin C. Kulik
review the literature on feedback timing and suggest that “Applied studies using
actual classroom quizzes and real learning materials have usually found
immediate feedback to be more effective than delay.”
6 Expertise researcher K.
Anders Ericsson agrees, arguing in favor of immediate feedback when it assists
in identifying and correcting mistakes and when it allows one to execute a
corrected version of their performance revised in response to the feedback.
7
Interestingly, laboratory studies tend to show that delaying the presentation of
the correct response along with the original task (delayed feedback) is more
effective. The simplest explanation of this result is that presenting the question
and answer again offers a second, spaced exposure to the information. If this
explanation were correct, all it would mean is that that immediate feedback is
best paired with delayed review (or further testing) to strengthen your memory
compared with a single exposure. I’ll cover more on spacing and how it impacts
your memory in the next chapter on retention.
Despite the superficially mixed results on the timing of feedback from the
scientific literature, I generally recommend faster feedback. This enables a
quicker recognition of mistakes. However, there’s a possible risk that this
recommendation might backslide into getting feedback before you’ve tried your
best to answer the question or solve the problem at hand. Early studies on
feedback timing tended to show a neutral or negative impact of immediate
feedback on learning. In those studies, however, experimenters often gave
subjects the ability to see the correct answer before subjects had finished filling
out the prompt.
8 That meant subjects could often copy the correct answer rather
than try to retrieve it. Feedback too soon may turn your retrieval practice
effectively into passive review, which we already know is less effective for
learning. For hard problems, I suggest setting yourself a timer to encourage you
to think hard on difficult problems before giving up to look at the correct
answer.
How to Improve Your Feedback
By now you see the importance of feedback to your learning efforts. I’ve
explained why feedback, especially when delivered to others, can sometimes
backfire. I’ve also showed how the three types—outcome, informational, and
corrective—have different strengths and the preconditions that need to be in
place in order to make them effective. Now I want to focus on some concrete
tactics you can apply to get better feedback.
Tactic 1: Noise Cancellation
Anytime you receive feedback, there are going to be both a signal—the useful
information you want to process—and noise. Noise is caused by random factors,
which you shouldn’t overreact to when trying to improve. Say you’re writing
articles that you post online, trying to improve your writing ability. Most of them
won’t attract much attention, and when they do, it’s often because of factors
outside of your control; for example, just the right person happens to share it,
causing it to spill across social networks. The quality of your writing does drive
these factors, but there’s enough randomness that you need to be careful not to
change your entire approach based on one data point. Noise is a real problem
when trying to improve your craft because you need to do far more work to get
the same information about how to write well. By modifying and selecting the
the same information about how to write well. By modifying and selecting the
streams of feedback you pay attention to, you can reduce the noise and get more
of the signal.
A noise-cancelling technique used in audio processing is filtering. Sound
engineers know that human speech tends to fall within a particular range of
frequencies, whereas white noise is all over the spectrum. They can boost the
signal, therefore, by amplifying the frequencies that occur in human speech and
quieting everything else. One way to do this is to look for proxy signals. These
don’t exactly equal success, but they tend to eliminate some of the noisy data.
For blog writing, one way to do so would be to use tracking code to figure out
what percentage of people read your articles all the way to the end. This doesn’t
prove your writing is good, but it’s a lot less noisy than raw traffic data.
Tactic 2: Hitting the Difficulty Sweet Spot
Feedback is information. More information equals more opportunities to learn. A
scientific measure of information is based on how easily you can predict what
message it will contain. If you know that success is guaranteed, the feedback
itself provides no information; you knew it would go well all along. Good
feedback does the opposite. It is very hard to predict and thus gives more
information each time you receive it.
The main way this impacts your learning is through the difficulty you’re
facing. Many people intuitively avoid constant failure, because the feedback it
offers isn’t always helpful. However, the opposite problem, of being too
successful, is more pervasive. Ultralearners carefully adjust their environment so
that they’re not able to predict whether they’ll succeed or fail. If they fail too
often, they simplify the problem so they can start noticing when they’re doing
things right. If they fail too little, they’ll make the task harder or their standards
stricter so that they can distinguish the success of different approaches.
Basically, you should try to avoid situations that always make you feel good (or
bad) about your performance.
Tactic 3: Metafeedback
Typical feedback is performance assessment: your grade on a quiz tells you
something about how well you know the material. However, there’s another type
of feedback that’s perhaps even more useful: metafeedback. This kind of
feedback isn’t about your performance but about evaluating the overall success
feedback isn’t about your performance but about evaluating the overall success
of the strategy you’re using to learn.
One important type of metafeedback is your learning rate. This gives you
information about how fast you’re learning, or at least how fast you’re
improving in one aspect of your skill. Chess players might track their Elo ratings
growth. LSAT studiers might track their improvements on mock exams.
Language learners might track vocabulary learned or errors made when writing
or speaking. There are two ways you can use this tool. One is to decide when
you should focus on the strategy you’re already using and when you should
experiment with other methods. If your learning rate is slowing to a trickle, that
might mean you’re hitting diminishing returns with your current approach and
could benefit from different kinds of drills, difficulties, or environments. A
second way you can apply metafeedback is by comparing two different study
methods to see which works better. During the MIT Challenge, I’d often split up
questions from different subtopics before testing myself on an exam and try
different approaches side by side. Does it work better to dive straight into trying
to answer questions or to spend a little time to try to see that you understand the
main concepts first? The only way you can know is to test your own learning
rates.
Tactic 4: High-Intensity, Rapid Feedback
Sometimes the easiest way to improve feedback is simply to get a lot more of it a
lot more often. This is particularly true when the default mode of learning
involves little or infrequent feedback. De Montebello’s strategy of improving
public speaking relied largely on getting far more frequent exposure to the stage
than most speakers do. Lewis’s language immersion exposes him to information
about his pronunciation at a point when most students still haven’t uttered a
word. High-intensity, rapid feedback offers informational advantages, but more
often the advantage is emotional, too. Fear of receiving feedback can often hold
you back more than anything. By throwing yourself into a high-intensity, rapid
feedback situation, you may initially feel uncomfortable, but you’ll get over that
initial aversion much faster than if you wait months or years before getting
feedback.
Being in such a situation also provokes you to engage in learning more
aggressively than you might otherwise. Knowing that your work will be
evaluated is an incredible motivator to do your best. This motivational angle for
committing to high-intensity feedback may end up outweighing the
committing to high-intensity feedback may end up outweighing the
informational advantage it provides.
Beyond Feedback
Receiving feedback isn’t always easy. If you process it as a message about your
ego rather than your skills, it’s easy to let a punch become a knockout. Though
carefully controlling the feedback environment so it is maximally encouraging
may be a tantalizing option, real life rarely affords such an opportunity. Instead,
it’s better to get in and take the punches early so that they don’t put you down
for the count. Though short-term feedback can be stressful, once you get into the
habit of receiving it, it becomes easier to process without overreacting
emotionally. Ultralearners use this to their advantage, exposing themselves to
massive amounts of feedback so that the noise can be stripped away from the
signal.
Feedback and the information it provides, however, is useful only if you
remember the lessons it teaches. Forgetting is human nature, so it is not enough
to learn; you also need to make the information stick. This brings us to the next
principle of ultralearning, retention, in which we’ll discuss strategies that will
ensure the lessons you learn aren’t forgotten.
Chapter X
Principle 7
Retention
Don’t Fill a Leaky Bucket
Memory is the residue of thought.
—Daniel Willingham, cognitive psychologist
In the small Belgian city of Louvain-la-Neuve, Nigel Richards has just won the
World Scrabble Championships. On its own, this isn’t too surprising. Richards
has won a championship three times before, and both his prowess with the game
and his mysterious personality have made him something of a legend in
competitive Scrabble circles. This time, however, is different: instead of the
original English-language version of the famous crossword game, Richards has
won the French World Championship. This is a much harder feat: most English
dictionary versions have roughly 200,000 valid word entries; French, with its
gendered nouns and adjectives and copious conjugations, has nearly double that
with around 386,000 valid word forms.
1 To pull off such a feat is quite
remarkable, even more so due to one simple fact: Richards doesn’t speak French.
Richards, an engineer born and raised in Christchurch, New Zealand, is an
unusual character. With his long beard and retro aviator sunglasses, he looks like
a cross between Gandalf and Napoleon Dynamite. His skills at Scrabble,
however, are no joke. A late starter to the game, his mother encouraged him to
start in his late twenties, saying “Nigel, since you’re no good at words, you
won’t be good at this game, but it will keep you occupied.”
2 From those
inauspicious beginnings Richards has gone on to dominate the competitive
Scrabble scene. Some people even argue that he may be the greatest player of all
time.
In case you’ve been living under a rock, Scrabble is based on forming
crosswords. Each player has seven letter tiles, drawn from a bag, with which to
form words. The catch is that the words must link up with the words already on
the board. To be a good player requires a voluminous memory, not only of the
words we use every day but of obscure words that are useful because of their
length or the letters they contain. A decent casual player quickly learns all the
valid two-letter words, including unusual ones such as “AA” (a type of lava) and
“OE” (a windstorm in the Faroe Islands). To perform at tournament level,
however, requires memorizing nearly all of the short words, as well as longer
seven- and eight-letter words, since if a player uses up all seven tiles in one turn,
there is an extra fifty-point bonus (or “bingo,” in Scrabble jargon). Memory,
however, isn’t the only skill needed. Like other competitive games, tournament
Scrabble uses a timing system, so skilled players must not only be able to
construct valid words from a scrambled set of tiles but quickly find spaces and
calculate which words will score the most points. In this regard, Richards is a
master: given the tiles CDHLRN and one blank (which can be used for any
letter), Richards ignored the obvious CHILDREN and instead linked up multiple
crosswords to make the even higher scoring CHLORODYNE.
Richards’s virtuosity is only intensified by the mystery that surrounds it. He is
quiet and mostly keeps to himself. He refuses all interviews with reporters and
seems completely uninterested in fame, fortune, or even providing explanations
for how he does it. A fellow competitor, Bob Felt, bumping into Richards at a
tournament noted his monklike serenity, telling him “When I see you, I can
never tell whether you’ve won or lost.” “That’s because I don’t care” was
Richard’s monotone response.
3 Even his competing in Belgium, which briefly
pulled him into the international media spotlight, was done as an excuse to do a
cycling trip through Europe. In fact, prior to his victory, he had spent only nine
weeks preparing. After he beat a Francophone player, Schelick Ilagou Rekawe
from Gabon, in the final match, he was given a standing ovation but needed a
translator to thank the audience.
What Is Nigel Richards’s Secret?
The more I read about Nigel Richards, the more intrigued I became. Richards
was as mysterious as he was incredible in his mnemonic abilities. He steadfastly
ignores opportunities for interviews and is famously laconic in descriptions of
his methods. After his victory in Louvain-la-Neuve, one reporter asked him if he
had any special methods for memorizing all those words. “No” was Richards’s
monosyllabic response. Still, even if he wouldn’t divulge his strategies publicly,
perhaps some digging could reveal clues.
The first thing I discovered was that although Richards’s victory in Belgium
was astounding, it wasn’t entirely without precedent. Other players of the game
have won World Championships without being fluent in the language of
competition. Scrabble is particularly popular in Thailand, for instance, and two
former world champions, Panupol Sujjayakorn and Pakorn Nemitrmansuk, are
not fluent in English. The reason is simple: remembering words in one’s native
language and remembering words in Scrabble are different mnemonic feats. In
spoken language, the meaning of a word, its pronunciation, and its feel are
important. In Scrabble, those things don’t matter; words are just combinations of
letters. Richards could win at French Scrabble without speaking French because
the game wasn’t much different from English; he just had to memorize different
patterns of letters. A native speaker has an advantage, of course, since many
spellings will already be familiar. But there will still be a large number of arcane
and unfamiliar words to memorize, and the skill of rearranging the letters into
valid board positions and calculating to achieve maximal points remains the
same in every language in which Scrabble can be played.
The next piece of the puzzle I discovered was that Scrabble, it turns out, isn’t
the only activity in which Richards possesses a strange intensity. His other love
is cycling. Indeed, in an early tournament in Dunedin, New Zealand, he got onto
his bicycle after work finished, pedaled through the night from Christchurch to
Dunedin, a distance of over two hundred miles, without sleeping, and started the
tournament first thing in the morning. After he won, competitors he met at the
tournament offered to give him a ride home. He politely declined, preferring to
bicycle back the entire way home to Christchurch for another sleepless night
before starting work again Monday morning.
4 At first that felt like just another
odd quirk in his profile, like his home-done haircuts and reluctance to be
interviewed. Now, though, I believe it may hold some keys to unlocking some of
his mystery.
Cycling, of course, isn’t a great mnemonic technique. If it were, Lance
Armstrong would have been a fierce contender. However, it does illustrate a
common theme in Richards’s personality that overlaps with that of other
ultralearners I have encountered: an obsessive intensity that exceeds what is
considered a normal investment of effort. Richards’s cycling, it turns out, also
lines up well with the only other clues I’ve been able to uncover about his
methods: he reads lists; long lists of words, starting with two-letter words and
then moving up. “The cycling helps,” he explains, “I can go through lists in my
mind.”
5 He reads the dictionary, focusing exclusively on combinations of letters,
ignoring definitions, tenses, and plurals. Then, drawing from memory, he repeats
them over and over again as he cycles for hours. This aspect also corresponds
with a method that is common to other ultralearners and that has shown up in
other principles of learning so far: active recall and rehearsal. By retrieving
words, Richards likely takes his already impressive memory and makes it
unassailable through active practice.
There are other clues about Richards’s performance: he focuses on memory,
not anagramming (rearranging the tiles to create words); he works forward and
backward, starting from small words, going on to big ones and back again; he
claims to recall the words visually, as he cannot remember words when they’re
spoken. All of these clues provide glimpses into Richards’s mind, but they leave
out even more than they reveal. How many times does he have to read the words
from his list before he can rehearse it mentally? Are the words organized in
some way or just listed alphabetically? Is he a savant with exceptional abilities
and lower-than-normal general intelligence or an all-round genius for whom
memorizing Scrabble words is just one of many impressive abilities? Maybe his
intelligence is quite average and his dominance in Scrabble represents his
extreme dedication to the game. We might never know the answers to those
questions.
I certainly can’t rule out the theory that Richards’s mind is simply hardwired
differently or better for memory than my own. After all, nothing I’ve
encountered so far about his method is so boldly original that serious Scrabble
players would be unaware of it. Yet Richards has completely dominated his
competition. Part of me suspects that his intense, obsessive personality, which
enables him to cycle for hours reviewing lists mentally, might also form at least
a partial explanation. Whatever gifts he might possess, he also seems to possess
the ultralearner ethos I’ve described thus far in the book. For whatever it is
worth, Richards himself argues for more of the latter than the former: “It’s hard
work, you have to have dedication to learn,”
6 elsewhere adding “I’m not sure
there is a secret, it’s just a matter of learning the words.”
7
Scrabble words may not be important to your life. However, memory is
essential to learning things well. Programmers must remember the syntax for the
commands in their code. Accountants need to memorize ratios, rules, and
regulations. Lawyers must remember precedents and statutes. Doctors need to
know tens of thousands of factoids, from anatomical descriptions to drug
interactions. Memory is essential, even when it is wrapped up in bigger ideas
such as understanding, intuition, or practical skill. Being able to understand how
something works or how to perform a particular technique is useless if you
cannot recall it. Retention depends on employing strategies so the things you
learn don’t leak out of your mind. Before discussing strategies of retention,
however, let’s take a look at why remembering things is so difficult.
Why Is It So Hard to Remember Things?
Richards is an extreme case, but his story nonetheless illustrates many themes
that are important for anyone who wants to learn something: How can you retain
all of the things you learn? How can you defend against forgetting hard-won
facts and skills? How can you store the knowledge you’ve acquired so that it can
be easily retrieved exactly when you need it? In order to understand learning,
you need to understand how and why you forget.
Losing access to previously learned knowledge has been a perennial problem
for educators, students, and psychologists. Fading knowledge impacts the work
you do as well. One study reported that doctors give worse medical care the
longer they have worked, as their stored knowledge from medical school is
gradually forgotten, despite their working in the profession full-time. Quoting
from the original abstract:
Physicians with more experience are generally believed to have accumulated
knowledge and skills during years in practice and therefore to deliver high-quality
care. However, evidence suggests that there is an inverse relationship between the
number of years that a physician has been in practice and the quality of care that
the physician provides.
8
Hermann Ebbinghaus, in one of the first psychological experiments in history,
spent years memorizing nonsense syllables, much in the same way Richards
memorizes Scrabble words, and carefully tracking his ability to recall them later.
memorizes Scrabble words, and carefully tracking his ability to recall them later.
From this original research, later verified by more experimentally robust studies,
Ebbinghaus discovered the forgetting curve. This curve shows that we tend to
forget things incredibly quickly after learning them, there being an exponential
decay in knowledge, which is steepest right after learning. However, Ebbinghaus
noted, this forgetting tapers off, and the amount of knowledge forgotten lessens
over time. Our minds are a leaky bucket; however, most of the holes are near the
top, so the water that remains at the bottom leaks out more slowly.
Over the intervening years, psychologists have identified at least three
dominant theories to help explain why our brains forget much of what we
initially learn: decay, interference, and forgotten cues. Though the jury is still
out on the exact mechanism underlying human long-term memory, these three
ideas likely form some part of explaining why we tend to forget things and,
conversely, provide insight into how we might better retain what we’ve learned.
Decay: Forgetting with Time
The first theory of forgetting is that memories simply decay with time. This idea
does seem to match common sense. We remember events, news, and things
learned in the past week much more clearly than things from last month. Things
learned this year are recalled with much greater accuracy than events from a
decade ago. By this understanding, forgetting is simply an inevitable erosion by
time. Like sands in an hourglass, our memories inexorably slip away from us as
we become more distant from them.
There are flaws with this theory being the complete explanation, however.
Many of us can vividly recall events from early childhood, even if we can’t
remember what we ate for breakfast last Tuesday. There also seem to be patterns
in which things are remembered and which are forgotten that go beyond the time
since they were originally learned: vivid, meaningful things are more easily
recalled than banal or arbitrary information. Even if there is a component to our
forgetting that is simply decay, it seems exceedingly unlikely that this is the only
factor.
Interference: Overwriting Old Memories with New Ones
Interference suggests a different idea: that our memories, unlike the files of a
computer, overlap one another in how they are stored in the brain. In this way,
memories that are similar but distinct can compete with one another. If you’re
learning programming, for instance, you may learn what a for loop is and
remember it in terms of doing something repeatedly. Later, you may learn about
while loops, recursion, repeat-until loops, and go-to statements. Now, each of
these has to do with doing something repeatedly, but in different ways, so they
may interfere with your ability to remember correctly what a for loop does.
There are at least two flavors of this: proactive interference and retroactive
interference. Proactive interference occurs when previously learned information
makes acquiring new knowledge harder. Think of this as if the “space” where
that information wants to be stored is already occupied, so forming the new
memory becomes harder. This can happen when you want to learn the definition
of a word but have difficulty because that word already has a different
association in your mind. Consider trying to learn the concept of negative
reinforcement in psychology—here the word “negative” has the meaning
“absent,” as opposed to “bad,” so negative reinforcement is when you encourage
a behavior by removing something, say a painful stimulus. However, since the
earlier meaning of negative as “bad” already exists, you may have difficulty
remembering this and it becomes easy to incorrectly equate negative
reinforcement with punishment. Retroactive interference is the opposite—where
learning something new “erases” or suppresses an old memory. Anyone who has
learned Spanish and later tried to learn French knows how tricky retroactive
interference can be, as French words pop out when you want to speak Spanish
again.
Forgotten Cues: A Locked Box with No Key
The third theory of forgetting says that many memories we have aren’t actually
forgotten but simply inaccessible. The idea here is that in order to say that one
has remembered something, it needs to be retrieved from memory. Since we
aren’t constantly experiencing the entirety of our long-term memories
simultaneously, this means there must be some process for dredging up the
information, given an appropriate cue. What may happen in this case is that one
of the links in the chain of retrieving the information has been severed (perhaps
by decay or interference) and therefore the entire memory has become
inaccessible. However, if that cue were restored, or if an alternative path to the
information could be found, we would remember much more than is currently
accessible to us.
accessible to us.
This explanation also has some advantages. Intuitively it seems to be
somewhat true, as we all know the tip-of-the-tongue experience, when we feel as
though we should be able to remember a fact or word but we’re not able to
summon it up immediately. It might also suggest that relearning things is much
faster than learning them initially, because relearning is closer to repair work,
while original learning is a completely new construction. Forgetting cues seems
highly likely as a partial, if not complete, explanation of forgetting many things.
Cue forgetting as a complete explanation for our memory woes isn’t without
its problems, however. Many memory researchers now believe that the act of
remembering is not a passive process. In recalling facts, events, or knowledge,
we’re engaging in a creative process of reconstruction. The memories
themselves are often modified, enhanced, or manipulated in the process of
remembering. It may be, then, that “lost” memories that are retrieved through
new cues are actually fabrications. This seems especially likely in the case of
“recovered” witness testimony from traumatic events, as experiments have
shown that even highly vivid memories that feel completely authentic to the
subject can be untrue.
9
How Can You Prevent Forgetting?
Forgetting is the default, not the exception, so the ultralearners I encountered had
devised various strategies for coping with this fact of life. These methods
roughly divide into tackling two similar but different problems. The first set of
methods deals with the problem of retention while undertaking the ultralearning
project: How can you retain the things you learned the first week, so that you
don’t need to relearn them by the last week? This is particularly important for
memory-intensive ultralearning efforts such as Benny Lewis’s language learning
and Roger Craig’s Jeopardy! trivia mastery. In these domains and many others,
the volume of information to be learned is often so large that the forgetting
becomes a practical obstacle almost immediately. The second set of methods, in
contrast, has to do with the longevity of the skills and knowledge acquired after
the project has been completed: Once a language has been learned to a level
you’re satisfied with, how can you keep yourself from forgetting it completely a
couple years later?
The ultralearners I encountered had devised differing methods for dealing
with these two problems, which varied in effort and intensity. Some, like Craig,
with these two problems, which varied in effort and intensity. Some, like Craig,
preferred elaborate electronic systems that can optimize memory with fancy
algorithms, leaving little waste and inefficiency, if at the cost of introducing
greater complexity. Others, like Richards, seem to prefer basic systems that
succeed on their simplicity.
You need to pick a mnemonic system, which will both accomplish your goals
and be simple enough to stick to. During intense periods of language learning,
the sheer volume of vocabulary often meant that spaced-repetition systems were
helpful for me. Other times, I preferred having conversations to maintain my
speaking ability, even though this method is not quite as precise. With other
subjects, I’m happier to allow for some degree of forgetting as long as I practice
the skills I need to use continuously and have the ability to relearn.
My approaches may not reach a theoretical ideal, but they may end up
working better because they have fewer possibilities for error and can be
sustained more easily. Regardless of the exact system used, however, all systems
seemed to work according to one of four mechanisms: spacing,
proceduralization, overlearning, or mnemonics. Let’s look at each of these
mechanisms of retention first, in order to make sense of the quite different and
idiosyncratic manifestations used in different ultralearning projects.
Memory Mechanism 1—Spacing: Repeat to Remember
One of the pieces of studying advice that is best supported by research is that if
you care about long-term retention, don’t cram. Spreading learning sessions over
more intervals over longer periods of time tends to cause somewhat lower
performance in the short run (because there is a chance for forgetting between
intervals) but much better performance in the long run. This was something I
needed to be careful about during the MIT Challenge. After my first few classes,
I switched from doing one class at a time to doing a few in parallel, to minimize
the impact that the crammed study time would have on my memory.
If you have ten hours to learn something, therefore, it makes more sense to
spend ten days studying one hour each than to spend ten hours studying in one
burst. Obviously, however, if the amount of time between study intervals gets
longer and longer, the short-term effects start to outweigh the long-term ones. If
you learn something with a decade separating study intervals, it’s quite possible
that you’ll completely forget whatever you had learned before you reach the
second session.
Finding the exact trade-off point between too long and too short has been a
minor obsession for some ultralearners. Space your study sessions too closely,
and you lose efficiency; space them too far apart, and you forget what you’ve
already learned. This has led many ultralearners to apply what are known as
spaced-repetition systems (SRS) as a tool for trying to retain the most knowledge
with the least effort. SRS was a major force behind Roger Craig’s Jeopardy!
trivia memorization, and I used the systems extensively when learning Chinese
and Korean. Although you may not have heard of this term, the general principle
is the backbone of many language-learning products, including Pimsleur,
Memrise, and Duolingo. These programs tend to hide the spacing algorithm in
the background, so you don’t need to bother yourself with it. However, other
programs, such as the open-source Anki, are the preferred tool of more extreme
ultralearners who want to squeeze out a little more performance.
SRS is an amazing tool, but it tends to have quite focused applications.
Learning facts, trivia, vocabulary words, or definitions is ideally suited for flash
card software, which presents knowledge in terms of a question with a single
answer. It’s more difficult to apply to more complicated domains of knowledge,
which rely on complex information associations that are built up only through
real-world practice. Still, for some tasks, the bottleneck of memory is so tight
that SRS is a powerful tool for widening it, even if there are some drawbacks.
The authors of a popular study guide for medical students center their approach
around SRS, because a medical student must remember so many things and the
default strategy of forgetting and relearning is quite costly in terms of time.
10
Spacing does not require complex software, however. As Richards’s story
clearly demonstrates, simply printing lists of words, reading them over, and then
rehearsing them mentally without having them in front of you is an incredibly
powerful technique. Similarly, semiregular practice of a skill is often quite
helpful. After my year of learning languages, I wanted to ensure that I didn’t
forget them. My approach was fairly simple: schedule thirty minutes of
conversation practice once a week, to be done over Skype using italki, an online
service for tutoring and language exchange partners all over the world. I
maintained this for one year, after which I dropped to once-per-month practice
for another two years. I don’t know whether this practice schedule was ideal, and
I had other opportunities to practice that came up spontaneously in that time
period that also helped, but I believe it was much better than doing nothing and
letting the skills atrophy. When it comes to retention, don’t let perfect become
the enemy of good enough.
Another strategy for applying spacing, which can work better for more
Another strategy for applying spacing, which can work better for more
elaborate skills that are harder to integrate into your daily habits, is to
semiregularly do refresher projects. I leaned toward this approach for the things I
learned during the MIT Challenge, since the skill I wanted most to retain was
writing code, which is tricky to do on only half an hour per week. This approach
has the disadvantage of sometimes deviating quite a lot from optimal spacing;
however, if you’re prepared to do a little bit of relearning to compensate, it can
still be a better approach than completely giving up practice. Scheduling this
kind of maintenance in advance can also be helpful, as it will remind you that
learning isn’t something done once and then ignored but a process that continues
for your entire life.
Memory Mechanism 2—Proceduralization: Automatic
Will Endure
Why do people say it’s “like riding a bicycle” and not “like remembering
trigonometry?” This common expression may be rooted in deeper neurological
realities than it first appears. There’s evidence that procedural skills, such as
riding a bicycle, are stored in a different way from declarative knowledge, such
as knowing the Pythagorean Theorem or the Sine Rule for triangles. This
difference between knowing how and knowing that may also have different
implications for long-term memory. Procedural skills, such as the everremembered bicycling, are much less susceptible to being forgotten than
knowledge that requires explicit recall to retrieve.
11
This finding can actually be used to our advantage. One dominant theory of
learning suggests that most skills proceed through stages—starting declarative
but ending up procedural as you practice more. A perfect example of this
declarative-to-procedural transition is typewriting. When you start typing on a
keyboard, you must memorize the positions of the letters. Each time you want to
type a word, you have to think in terms of its letters, recall each one’s position
on the keyboard, and then move your finger to that spot to press it. This process
may fail; you may forget where a key is and need to look down to type it.
However, if you practice more and more, you stop having to look down.
Eventually you stop having to think about the letters’ positions or how to move
your fingers to meet them. You may even reach a point where you don’t think of
letters at all and whole words come out at a time. Such procedural knowledge is
quite robust and tends to be retained much longer than declarative knowledge. A
quick observation is enough to verify this: when you’ve gotten really good at
typing and someone asks you to quickly say where on a keyboard the letter w is,
you might need to actually put your hands in the keyboard position (or imagine
you’re doing so) and pretend to type the w to say definitively. This is exactly
what happened to me as I was typing out this paragraph. What has happened is
that what was originally the primary access point to knowledge, your explicit
memory of the key location, has faded away and now needs to be recalled with
the more durable procedural knowledge encoded in your motor movements. If
you’ve ever had to enter a password or pin code you use often, you may be in a
similar position, where you remember it by feel and not by its explicit
combination of numbers and letters.
Because of the fact that procedural knowledge is stored for longer, this may
suggest a useful heuristic. Instead of learning a large volume of knowledge or
skills evenly, you may emphasize a core set of information much more
frequently, so that it becomes procedural and is stored far longer. This was an
unintentional side effect of my friend’s and my language-learning project. Being
forced to speak a language constantly meant that a core set of phrases and
patterns was repeated so often that neither of us will ever forget them. This may
not hold true for a bunch of less frequently used words or phrases, but the
starting points of conversations are nearly impossible to forget. The classic
approach to language studies, in which students “move on” from beginner words
and grammatical patterns to more complicated ones may sidestep this, so that
those core patterns aren’t sticky enough to last for years without repeated
practice.
Failing to fully proceduralize core skills was a major flaw of my first major selfeducation effort, the MIT Challenge, which I was able to improve upon in my
subsequent language-learning and portrait-drawing projects. Whereas the MIT
Challenge did have core mathematical and programming skills that were often
repeated, what ended up being proceduralized was more haphazard rather than
reflecting a conscious decision to automate the most essential skills of applying
computer science.
Most skills we learn are incompletely proceduralized. We may be able to do
some of them automatically, but other parts require us to actively search our
minds. You might, for instance, be able to easily move variables from one side
of an equation to the other in algebra without thinking. But you may have to
think a bit more when exponents or trigonometry is involved. Perhaps, owing to
think a bit more when exponents or trigonometry is involved. Perhaps, owing to
their nature, some skills cannot be completely automated and will always require
some conscious thought. This creates an interesting mix of knowledge, with
some things retained quite stably over longer periods of time and others
susceptible to being forgotten. One strategy for applying this concept might be to
ensure that a certain amount of knowledge is completely proceduralized before
practice concludes. Another approach might be to spend extra effort to
proceduralize some skills, which will serve as cues or access points for other
knowledge. You may aim to completely proceduralize the process you use to
start working on a new programming project, for example, so that you can get
over that hump in the process of writing a new program. These strategies are
somewhat speculative, but I think there are lots of potential ways the declarativeto-procedural transition of knowledge might be applied by clever ultralearners in
the future.
Memory Mechanism 3—Overlearning: Practice Beyond
Perfect
Overlearning is a well-studied psychological phenomenon that’s fairly easy to
understand: additional practice, beyond what is required to perform adequately,
can increase the length of time that memories are stored.
12 The typical
experimental setup is to give subjects a task, such as assembling a rifle or going
through an emergency checklist, allowing them enough time to practice that they
can do it correctly once. The time from zero to this point is considered the
“learning” phase. Next, allow the subjects different amounts of “overlearning,”
or practice that continues after the first correct application. Since subjects are
already doing the skill correctly, performance doesn’t improve past this point.
However, the overlearning can extend the durability of the skill.
In the typical setting in which overlearning has been studied, the duration of
the overlearning effects tends to be quite short; practicing a little longer in one
session produces an additional week or two of recall. This may imply that
overlearning is primarily a short-term phenomenon: something useful for skills
like first aid or emergency response protocols, which are rarely practiced but
need to be kept fresh in between regular training sessions. I suspect, however,
that overlearning might have longer-term implications if it is combined with
spacing and proceduralization over much longer projects. In my own personal
experience drawing portraits, for instance, the thought process used for mapping
out the facial features I learned from Vitruvian Studio was repeated so many
out the facial features I learned from Vitruvian Studio was repeated so many
times that it’s hard to forget, even though my major practice time was only
during one month. Similarly, certain reflexes of programming or mathematics I
can still easily recall from my MIT Challenge days, even without practice in the
interim, because they happened to be patterns that were repeated far more than
was necessary to perform them adequately at the time (because they were
components of more elaborate problems).
Overlearning dovetails nicely with the principle of directness. Because direct
use of a skill frequently involves overpracticing certain core abilities, that kernel
is usually quite resistant to forgetting, even years later. In contrast, academically
learned subjects tend to distribute practice more evenly to cover the entire
curriculum to a minimum level of competency in each area, regardless of the
centrality of subtopics to practical applications. Many people I’ve known who
have learned a language that I also speak but who learned it through years of
formal schooling have much more impressive vocabularies or knowledge of
grammatical nuances than I do. However, those same people may trip over fairly
basic phrases, because they learned every fact and skill evenly, rather than
overlearning the smaller subset of very common patterns.
There seem to be two main methods I’ve encountered for applying
overlearning. The first is core practice, continually practicing and refining the
core elements of a skill. This approach often works well paired with some kind
of immersion or working on extensive (as opposed to intensive) projects, after
the initial ultralearning phase has been completed. The shift from learning to
doing here may actually involve a deeper, subtler form of learning, which
shouldn’t be discounted as simply applying previously learned knowledge.
The second strategy is advanced practice, going one level above a certain set
of skills so that core parts of the lower-level skills are overlearned as one applies
them in a more difficult domain. One study of algebra students demonstrated this
second strategy.
13 Most students who had taken an algebra class and were
retested years later had forgotten huge amounts of what they had learned. This
could have been either because the information was truly lost or simply because
forgotten cues rendered the majority of it inaccessible. Interestingly, this rate of
forgetting was the same for better- and poorer-performing students; better
students retained more than weaker ones, but the rate at which they had forgotten
was the same. One group, however, did not show such a steep decline in
forgetting: those who had taken calculus. This suggests that moving up a level to
a more advanced skill enabled the earlier skill to be overlearned, thus preventing
some forgetting.
Memory Mechanism 4—Mnemonics: A Picture Retains a
Thousand Words
The final tool common to many ultralearners I encountered was mnemonics.
There are many mnemonic strategies, and covering them all is outside the scope
of this book. What they have in common is that they tend to be hyperspecific—
that is, they are designed to remember very specific patterns of information.
Second, they usually involve translating abstract or arbitrary information into
vivid pictures or spatial maps. When mnemonics work, the results can be almost
difficult to believe. Rajveer Meena, the Guinness World Record holder for
memorizing digits of the mathematical constant pi, knows the number to 70,000
decimal places.
14 Master mnemonicists, who compete in championships of
memory, can memorize the order of a deck of cards in under sixty seconds and
can repeat a poem verbatim after only a minute or two of studying. These feats
are quite impressive, and even better, they can be learned by anyone patient
enough to apply them. How do they work?
One common, and useful, mnemonic is known as the keyword method. The
method works by first taking a foreign-language word and converting it into
something it sounds like in your native language. If I were doing this with
French, for example, I might take the word chavirer (to capsize) and convert it
into “shave an ear,” to which it is close enough in sound for the latter to serve as
an effective cue for recalling the original word. Next I create a mental image that
combines the sounds-like version of the foreign word and an image of its
translation in a fantastical and vivid setting that is bizarre and hard to forget. In
this case, I could imagine a giant ear shaving a long beard while sitting in a boat
that capsizes. Then, whenever I need to remember what “capsize” is in French, I
think of capsizing, recall my elaborate picture, which links to “shaving an ear”
and thus . . . chavirer. This process sounds needlessly complicated and elaborate
at first, but it benefits from converting a difficult association (between arbitrary
sounds and a new meaning) into a few links that are much easier to associate and
remember. With practice, each conversion of this type may take only fifteen to
twenty seconds, and it really does help with remembering foreign-language
words. This particular kind of mnemonic works for this purpose, but there are
others that work for remembering lists, numbers, maps, or sequences of steps in
a procedure. For a good introduction to this topic, I highly recommend Joshua
Foer’s book Moonwalking with Einstein: The Art and Science of Remembering
Everything.
Mnemonics work well, and with practice, anyone can do them. Why, then, are
they not front and center in this chapter, instead of at the end? I believe that
mnemonics, like SRS, are incredibly powerful tools. And as tools, they can open
new possibilities for people who are not familiar with them. However, as
someone who has spent much time exploring them and applying them to realworld learning, their applications are quite a bit narrower than they first appear,
and in many real-world settings they simply aren’t worth the hassle.
I believe there are two disadvantages to mnemonics. The first is that the most
impressive mnemonics systems (like the one for memorizing thousands of digits
of the mathematical constant pi), also require a considerable up-front investment.
After you’re done, you can memorize digits easily, but this isn’t actually a very
useful task. Most of our society adapts around the fact that people generally
cannot memorize digits, so we have paper and computers do it for us. The
second disadvantage is that recalling from mnemonics is often not as automatic
as directly remembering something. Knowing a mnemonic for a foreignlanguage word is better than failing to remember it entirely, but it’s still too slow
to allow you to fluently form sentences out of mnemonically remembered words.
Thus mnemonics can act as a bridge for difficult-to-remember information, but
it’s usually not the final step in creating memories that will endure forever.
Mnemonics, therefore, are an incredibly powerful if somewhat brittle tool. If
you are doing a task that requires memorizing highly dense information in a very
specific format, especially if the information is going to be used over a few
weeks or months, they can enable you to do things with your mind that you
might not have thought possible. Alternatively, they can be used as an
intermediate strategy to smooth initial information acquisition when the
information is quite dense. I’ve found them useful for language learning and
terminology, and, paired with SRS, they can form an effective bridge from
feeling as though there’s no way you can possibly remember everything to
remembering it so deeply that you can’t possibly forget. Indeed, in a world
before paper, computers, and other externalized memories, mnemonics were the
main game in town. However, in the modern world, which has developed
excellent coping mechanisms for the fact that most people cannot remember
things as a computer can, I feel that mnemonics tend to serve more as cool tricks
than as a foundation you should base your learning efforts on. Still, there is a
devoted subset of ultralearners who are fiercely committed to applying these
devoted subset of ultralearners who are fiercely committed to applying these
techniques, so my word shouldn’t be the final verdict.
Winning the War Against Forgetting
To retain knowledge is ultimately to combat the inevitable human tendency to
forget. This process occurs in all of us, and there’s no way to avoid it
completely. However, certain strategies—spacing, proceduralization,
overlearning, and mnemonics—can counteract your short- and long-term rates of
forgetting and end up making a huge difference in your memorization.
I opened this chapter by discussing Nigel Richards’s mysterious Scrabble
mastery. How he is able to recall so many words so quickly and see them in a set
of scrambled tiles will likely remain an enigma. What we do know about him fits
the picture of other ultralearners who have dominated memory-intensive
subjects: active recall, spaced rehearsal, and an obsessive commitment to intense
practice. Whether you or I have the will to go as far as Richards does is an open
question, but with hard work and a good strategy, it seems likely to me that the
battle against forgetting need not be a losing one.
Though Richards’s Scrabble practice may give him the benefit of memorizing
words he doesn’t know the meaning of, real life tends to reward a different kind
of memory: one that integrates knowledge into a deep understanding of things.
In the next principle, we’ll look at going from memory to intuition.
Chapter XI
Principle 8
Intuition
Dig Deep Before Building Up
Do not ask whether a statement is true until you know what it means.
—Errett Bishop, mathematician
To the world, he was an eccentric professor and Nobel Prize–winning physicist;
to his biographer, he was a genius; but to those who knew him, Richard
Feynman was a magician. His colleague the mathematician Mark Kac once
posited that the world holds two types of geniuses. The first are ordinary
geniuses: “Once we understand what they have done we feel certain that we, too,
could have done it.” The other type are magicians, whose minds work in such
inscrutable ways that “Even after we understand what they have done, the
process by which they have done it is completely dark.” Feynman, by his
reckoning, was “a magician of the highest caliber.”
1
Feynman could take problems others had worked on for months and
immediately see the solution. In high school, he competed in mathematics
tournaments, where he would often get the correct answer while the problem
was still being stated. While his competitors had just begun to compute,
Feynman already had the answer circled on the page. In his college days, he
competed in the Putnam Mathematics Competition, with the winner receiving a
paid scholarship to Harvard. This competition is notoriously difficult, requiring
clever tricks rather than straightforward application of previously learned
principles. Time is also a factor, and some examination sessions have a median
score of zero, meaning the typical competitor didn’t get even one right. Feynman
walked out of the exam early. He scored first place, with his fraternity brothers
later being amazed at the drastic gap between Feynman’s score and the next four
on the list. During his work on the Manhattan Project, Niels Bohr, then one of
the most famous and important living physicists, asked to speak with Feynman
directly, to run his ideas by the young grad student before talking with the other
physicists. “He’s the only guy who’s not afraid of me” was Bohr’s explanation.
“[He] will say when I’ve got a crazy idea.”
2
Nor was Feynman’s magic restricted to physics. As a child he went around
fixing people’s radios, in part because paying an adult for repairs in the
Depression was too costly but also because the radio owners marveled at his
process. Once, while he was lost in thought trying to figure out why a radio was
producing an awful noise as it started up, the owner of the radio got impatient.
“What are you doing? You come to fix the radio, but you’re only walking back
and forth!” “I’m thinking!” came the reply, at which the owner, startled at the
boldness for which Feynman would later become famous, laughed. “He fixes
radios by thinking!”
As a young man during the construction of the atomic bomb in the Manhattan
Project, he occupied his free time picking the locks of his supervisors’ desks and
cabinets. He once broke into a senior colleague’s filing cabinet, where the
secrets for building a nuclear bomb were kept, as a practical joke. Another time,
he demonstrated his technique to a military official, who, instead of fixing the
security flaw, decided the proper course was to warn everyone to keep Feynman
away from their safes! Later, upon meeting a locksmith, he found that his
reputation had grown to the point where the professional said, “God! You’re
Feynman—the great safecracker!”
He also created the impression of being a human calculator. On a trip to
Brazil, he went toe to toe against an abacus salesman, computing difficult figures
such as the cube root of 1,729.03. Not only did Feynman get the right answer,
12.002, but he got it to more decimal places than the abacus salesman, who was
still furiously calculating to get to 12 when Feynman displayed his five-digit
result. This ability impressed even other professional mathematicians, to whom
he argued that he could, within one minute, get the answer to any problem that
could be stated in ten seconds to within 10 percent of the correct number. The
mathematicians threw questions at him such as “e to the power of 3.3” or “e to
the power of 1.4,” and Feynman managed to spit back the correct answer almost
immediately.
Demystifying Feynman’s Magic
Feynman was certainly a genius. Many people, including his biographer James
Gleick, are satisfied to leave it at that. A magic trick, after all, is most dazzling
when you don’t know how it is done. Perhaps this is why many accounts of the
man have focused on his magic instead of his method.
Though Feynman was quite smart, his magic had its gaps. He excelled in math
and physics but was abysmal in the humanities. His college grades in history
were in the bottom fifth of his class, in literature in the bottom sixth, and his fine
arts grades were worse than those of 93 percent of his fellow students. At one
point, he even resorted to cheating on a test to pass. His intelligence, measured
while he was in school, scored 125. The average college graduate has a score of
115, which puts Feynman only modestly higher. Perhaps, as has been argued
afterward, Feynman’s genius failed to be captured in his IQ score, or it simply
was a poorly administered test. However, for someone so celebrated for a mind
beyond comprehension, these facts remind us that Feynman was mortal.
What about Feynman’s mental calculus? In this case, we have Feynman’s
words himself for how he could compute so much faster than the abacus
salesman or his mathematician colleagues. The cube root of 1,729.03? Feynman
explained, “I happened to know that a cubic foot contains 1728 cubic inches, so
the answer is a tiny bit more than 12. The excess, 1.03, is only one part in nearly
2000, and I had learned in calculus that for small fractions, the cube root’s
excess is one-third of the number’s excess. So all I had to do was find the
fraction 1/1728, and multiply by 4.” The constant e to the power of 1.4?
Feynman revealed, “because of radioactivity (mean-life and half-life), I knew the
log of 2 to the base e, which is .69315 (so I also knew that e to the power of .7 is
nearly equal to 2).” To go to the power of 1.4, he’d just have to multiply that
number against itself. “[S]heer luck,” he explained.
3 The secret was his
impressive memory for certain arithmetic results and an intuition with numbers
that enabled him to interpolate. However, the lucky picks of his examiners
allowed him to leave an impression of a magical ability to calculate.
How about the famous lock picking? Once again, it was magic, in the same
How about the famous lock picking? Once again, it was magic, in the same
sense as a magician performing well-practiced tricks. He obsessed over figuring
out how combination locks worked. One day he realized that by fiddling with a
lock when it was open, he could figure out the last two numbers on the safe. He
would write them down on a note after he left the person’s office and then could
sneak back in, crack the remaining number with some patience, and leave
ominous notes behind.
Even his magical intuition for physics had its explanation: “I had a scheme,
which I still use today when somebody is explaining something that I’m trying
to understand: I keep making up examples.” Instead of trying to follow an
equation, he would try to imagine the situation it described. As more information
was given, he’d work it through on his example. Then whenever his interlocutor
made a mistake, he could see it. “As they’re telling me the conditions of the
theorem, I construct something which fits all the conditions. You know, you
have a set (one ball)—disjoint (two balls). Then the balls turn colors, grow hairs,
or whatever, in my head as they put more conditions on. Finally they state the
theorem, which is some dumb thing about the ball which isn’t true for my hairy
green ball thing, so I say, ‘False!’”
4
Magic, perhaps, Feynman did not possess, but an incredible intuition for
numbers and physics he certainly did. This might downplay the idea that his
mind worked in a fundamentally different way from yours or mine, but it doesn’t
negate the impressiveness of his feats. After all, even knowing the logic behind
Feynman’s sleight of hand, I’m certain I wouldn’t have been able to calculate the
numbers he did so effortlessly or follow some complex theory in my mind’s eye.
This explanation doesn’t provide the satisfying “Aha!” that it would have had
the magician’s trick been revealed as something trivial. Therefore, we need to
dig deeper to an understanding of how someone such as Feynman could develop
this incredible intuition in the first place.
Inside the Mind of the Magician
Psychological researchers have investigated the problem of how intuitive
experts, such as Feynman, think differently about problems than novices do. In a
famous study, advanced PhDs and undergraduate physics students were given
sets of physics problems and asked to sort them into categories.
5
Immediately, a
stark difference became apparent. Whereas beginners tended to look at
superficial features of the problem—such as whether the problem was about
pulleys or inclined planes—experts focused on the deeper principles at work.
“Ah, so it’s a conservation of energy problem,” you can almost hear them saying
as they categorized the problem by what principles of physics they represented.
This approach is more successful in solving problems because it gets to the core
of how the problems work. The surface features of a problem don’t always relate
to the correct procedure needed to solve it. The students needed much more trial
and error to home in on the correct method, whereas the experts could
immediately start with the right approach.
If the principles-first way of thinking of problems is so much more effective,
why don’t students start there instead of attending to superficial characteristics?
The simple answer may be that they can’t. Only by developing enough
experience with problem solving can you build up a deep mental model of how
other problems work. Intuition sounds magical, but the reality may be more
banal—the product of a large volume of organized experience dealing with the
problem.
Another study, this time comparing chess masters and beginners, offered an
explanation of why this might be so.
6 The memory for chess positions of experts
and novices was tested by showing them a particular chess setup and then asking
them to re-create it on an empty board. The masters could recall far more than
the beginners. The new players needed to put down pieces one by one and were
often unable to fully remember all the details of the position. The masters, in
contrast, remembered the board in larger “chunks” with several pieces
corresponding to a recognizable pattern put down at the same time.
Psychologists theorize that the difference between grand masters and novices is
not that grand masters can compute many more moves ahead but that they have
built up huge libraries of mental representations that come from playing actual
games. Researchers have estimated that having around 50,000 of these mental
“chunks” stored in long-term memory is necessary to reach expert status.
7 These
representations allow them to take a complex chess setup and reduce it to a few
key patterns that can be worked with intuitively. Beginners, who lack this
ability, have to resort to representing each piece as a single unit and are therefore
much slower.*
This facility of chess grand masters, however, is limited to the patterns that
come from real chess games. Give beginners and experts a randomized chess
board (one that doesn’t arise from normal play), and the experts no longer
display the same marked advantage. Without the library of memorized patterns
at their disposal, they have to resort to the beginner’s method of remembering
at their disposal, they have to resort to the beginner’s method of remembering
the board piece by piece.
This research gives us a glimpse into how the mind of a great intuitionist such
as Feynman operated. He, too, focused on principles first, building off examples
that cut straight to the heart of what the problem represented rather than focusing
on superficial features. His ability to do this was also built off an impressive
library of stored physics and math patterns. His mental calculation feats seem
impressive to us but were trivial to him, because he happened to know so many
mathematical patterns. Like chess grand masters, when given real physics
problems he excelled because he had built a huge library of patterns from real
experiences with physics. However, his intuition, too, would fail him when the
subject of his study wasn’t built on those assumptions. Feynman’s
mathematician friends would test him on counterintuitive theorems from
mathematics. His intuition there would fail when properties of the procedure
(such as that an object can be cut into infinitely small pieces) defied the normal
physical limitations that aided his intuition elsewhere.
Feynman’s magic was his incredible intuition, coming from years of playing
with the patterns of math and physics. Could emulating his approach to learning
enable someone else to capture some of that magic? Let’s look at some of
Feynman’s hallmark approaches to learning and solving problems and try to
reveal some of the magician’s secrets.
How to Build Your Intuition
Simply spending a lot of time studying something isn’t enough to create a deep
intuition. Feynman’s own experience demonstrates this. On numerous occasions,
he would encounter students who memorized solutions to a particular problem
but failed to see how they applied outside the textbook domain. In one story, he
tricked some of his classmates into believing that a French curve (a device for
drawing curved lines) was special because, no matter how you hold it, the
bottom is tangent to a horizontal line. This, however, is true of any smooth
shape, and it is an elementary fact of calculus that his fellow classmates should
have realized. Feynman saw this as an example of a particularly “brittle” way of
learning things, since students didn’t really think about relating what they had
learned to problems outside the textbook.
How, then, can someone avoid a similar fate—spending a lot of time learning
something without really developing the flexible intuition for it that made
something without really developing the flexible intuition for it that made
Feynman famous? There’s no precise recipe for doing so, and a healthy dose of
experience and smarts certainly helps. However, Feynman’s own account of his
learning process offers some useful guidelines for how he did things differently.
Rule 1: Don’t Give Up on Hard Problems Easily
Feynman was obsessed with solving problems. Starting in his childhood days of
tinkering with radios, he would work stubbornly on a problem until it yielded.
Sometimes, when the owner of the radio would get impatient, he recalled, “If
[he] had said, ‘Never mind, it’s too much work,’ I’d have blown my top, because
I want to beat this damn thing, as long as I’ve gone this far.”
8 This tendency
carried over into mathematics and physics. He’d often eschew easier methods,
such as the Lagrangian technique, forcing himself to painstakingly calculate all
the forces by hand, simply because with the latter method he came to understand
it better. Feynman was a master at pushing farther on problems than others
expected of him, and this itself might have been the source of many of his
unorthodox ideas.
One way you can introduce this into your own efforts is to give yourself a
“struggle timer” as you work on problems. When you feel like giving up and that
you can’t possibly figure out the solution to a difficult problem, try setting a
timer for another ten minutes to push yourself a bit further. The first advantage
of this struggle period is that very often you can solve the problem you are faced
with if you simply apply enough thinking to it. The second advantage is that
even if you fail, you’ll be much more likely to remember the way to arrive at the
solution when you encounter it. As mentioned on the chapter on retrieval,
difficulty in retrieving the correct information—even when the difficulty is
caused by the information not being there—can prime you to remember
information better later.
Rule 2: Prove Things to Understand Them
Feynman told a story of his first encounter with the work by the physicists T. D.
Lee and C. N. Yang.
9 “I can’t understand these things that Lee and Yang are
saying. It’s all so complicated,” he declared. His sister, lightly teasing him,
remarked that the problem wasn’t that he couldn’t understand it but that he
hadn’t invented it. Afterward, Feynman decided to read through the papers
meticulously, finding that they weren’t actually so difficult but that he had
simply been afraid to go through them.
Though this story illustrates one of Feynman’s quirks, it is also revealing
because it illustrates a major point in his method. Feynman didn’t master things
by following along with other people’s results. Instead, it was by the process of
mentally trying to re-create those results that he became so good at physics. This
could be a disadvantage at times, since it caused him to repeat work and reinvent
processes that already existed in other forms. However, his drive to understand
things by virtue of working through the results himself also assisted in building
his capacity for deep intuition.
Feynman was not alone in this approach. Albert Einstein, as a child, built his
intuitive powers by trying to prove propositions in math and physics. One of
Einstein’s earliest mathematical forays was trying to prove the Pythagorean
Theorem on the basis of similar triangles.
10 What this approach indicates is that
both men had a tendency to dig much deeper before they considered something
to be “understood.” Feynman’s scoffing at not understanding Lee and Yang
wasn’t because there was no understanding; indeed, he was familiar with much
of the background work on the problem. Instead, it was probably because his
notion of understanding was much deeper and more based on demonstrating
results himself, rather than merely nodding along while reading.
The challenge of thinking you understand something you don’t is
unfortunately a common one. Researcher Rebecca Lawson calls this the “illusion
of explanatory depth.”
11 At issue here is the notion that we judge our own
learning competency, not directly but through various signals. Assessing
whether or not we know a factual matter, such as what is the capital of France, is
quite easy—either the word “Paris” comes up in your mind, or it doesn’t. Asking
whether you understand a concept is a lot harder because you may understand it
a little, but not enough for the purposes at hand.
Here’s a perfect thought experiment to help you understand the problem. Get
out a piece of paper, and try, briefly, to sketch how a bicycle looks. It doesn’t
need to be a work of art; just try to place the seat, handles, tires, pedals, and bike
chain in the right place. Can you do it?
Don’t cheat by just trying to visualize the bicycle. Actually see if you can
draw it. If you don’t have a pencil or paper handy, you can simulate it by saying
which things connect to what. Have you tried it?
Interestingly, Rebecca Lawson’s study asked participants to do exactly this.
As the illustrations clearly show, most participants had no idea how the
machines were assembled, even though they used them all the time and believed
they understood them quite well. The illusion of understanding is very often the
barrier to deeper knowledge, because unless that competency is actually tested,
it’s easy to mislead yourself into thinking you understand more than you do.
Feynman’s and Einstein’s approach to understanding propositions by
demonstrating them prevents this problem in a way that’s hard to do otherwise.
12
Were you one of the lucky ones who managed to put the chains on correctly?
Try the exercise again, except this time with a can opener. Can you explain how
it works? How many gears are there? How does it cut the lid open? This one is
much harder, yet most of us would say we understand can openers!
Rule 3: Always Start with a Concrete Example
Human beings don’t learn things very well in the abstract. As the research on
transfer demonstrates, most people learn abstract, general rules only after being
exposed to many concrete examples. It’s not possible to simply present a general
principle and expect that you can apply it to concrete situations. As if presaging
this observation, Feynman himself would supply concrete examples even when
they were not given. Working through an explicit example in his mind’s eye, he
could follow along and see what the math was trying to demonstrate.
This process of following along with one’s own example forces a deeper level
of processing the material as it is being presented. A finding from the literature
on memory, known as the levels-of-processing effect, suggests that it isn’t
simply how much time you spend paying attention to information that
determines what you retain but, crucially, how you think about that information
while you pay attention to it. In one study of this effect, participants were asked
to review a list of words; half of them were told it would be for a test (and thus
they were motivated to learn it), while the others were simply told to review the
list.
13 Within each group, participants were again split by what orienting
technique they used to review the list. Half were asked to notice whether or not
the words contained the letter e, a relatively shallow level of processing, while
the others were asked if the word was pleasant or not, a deeper processing of the
meaning of the word, not merely its spelling. The result was that motivation
played no difference; telling students to study for a test didn’t impact how much
they retained. However, the orienting technique did make a large difference.
Those who processed the words deeply remembered almost twice as much as
those who simply scanned their spelling.
14
Feynman’s habit of developing a concrete instance of a problem can be seen
as an example of this deeper form of processing, which not only enhances later
retention but also fosters an intuitive understanding. This technique also enables
some feedback, because when it’s not possible to imagine an appropriate
example, that’s evidence that you don’t understand something well enough and
would benefit from going back a few steps and learning the material better
before continuing. Using feedback-rich processes to test whether or not he knew
something was a hallmark of Feynman’s learning style.
Rule 4: Don’t Fool Yourself
“Don’t fool yourself” was one of Feynman’s most popular aphorisms, to which
he added, “and you’re the easiest person to fool.” He was deeply skeptical of his
own understanding. He presaged the current replication crisis in psychology,
attacking what he perceived as many social scientists fooling themselves into
believing they had discovered something they had not. I suspect that part of this
believing they had discovered something they had not. I suspect that part of this
insight arose from the fact that he had cultivated such rigorous standards for
what he counted as knowing.
The Dunning-Kruger effect occurs when someone with inadequate
understanding of a subject nonetheless believes he or she possesses more
knowledge about the subject than the people who actually do.
15 This can occur
because when you lack knowledge about a subject, you also tend to lack the
ability to assess your own abilities. It is true that the more you learn about a
subject, the more questions arise. The reverse also seems to be true, in that the
fewer questions you ask, the more likely you are to know less about the subject.
One way to avoid this problem of fooling yourself is simply to ask lots of
questions. Feynman took this approach himself: “Some people think in the
beginning that I’m kind of slow and I don’t understand the problem, because I
ask a lot of these ‘dumb’ questions: ‘Is a cathode plus or minus? Is an an-ion this
way, or that way?’”*
16 How many of us lack the confidence to ask “dumb”
questions? Feynman knew he was smart and had no problem asking them. The
irony is that by asking questions with seemingly obvious answers, he also
noticed the not-so-obvious implications of the things he studied.
The opposite tendency, to avoid asking questions in the vain attempt to appear
knowledgeable, has considerable costs. While lecturing in Brazil, Feynman’s
students would often complain when he asked simple questions that they knew
the answers to already, instead of just lecturing. Why waste valuable classroom
time on such exercises? The answer, Feynman eventually realized, was that they
didn’t know the answers but didn’t want to admit it in front of everyone else in
the class, wrongly assuming that they were the only ones who didn’t know it.
Explaining things clearly and asking “dumb” questions can keep you from
fooling yourself into thinking you know something you don’t.
The Feynman Technique
When I first read about Feynman, I was inspired to try to formulate many of
these different observations into a concrete method I could apply to my own
studies. What resulted was something I named the Feynman Technique and
applied extensively during my MIT Challenge. The purpose of using this
technique is to help develop intuition about the ideas you are learning. It can be
used when you don’t understand an idea at all or simply when you understand
something a little but really want to turn it into a deep intuition.
something a little but really want to turn it into a deep intuition.
The method is quite simple:
1. Write down the concept or problem you want to understand at the top of a
piece of paper.
2. In the space below, explain the idea as if you had to teach it to someone
else.
a. If it’s a concept, ask yourself how you would convey the idea to
someone who has never heard of it before.
b. If it’s a problem, explain how to solve it and—crucially—why that
solution procedure makes sense to you.
3. When you get stuck, meaning your understanding fails to provide a clear
answer, go back to your book, notes, teacher, or reference material to find
the answer.
The crux of this method is that it tries to dispel the illusion of explanatory
depth. Since many of our understandings are never articulated, it’s easy to think
you understand something you don’t. The Feynman Technique bypasses this
problem by forcing you to articulate the idea you want to understand in detail.
Just as drawing a bicycle quickly confirms whether you have a basic grasp of
how it is put together, using this technique will quickly reveal how much you
really understand of your subject. Now any gaps in your understanding will
become obvious as you struggle to explain key parts of the idea.
The technique itself has some nuances and can be applied in a few different
ways that might be helpful, depending on your specific intuitive deficit.
Application 1: For Things You Don’t Understand at All
The first way to use this approach is when you don’t understand something at
all. In this case, the easiest way is to do it with the book in hand and go back and
forth between your explanation and the one in the book. This lacks the benefits
of retrieval practice, but it can often be essential when the explanation you’ve
been given baffles you. Feynman himself did something similar when presented
with what he saw to be philosophical gobbledygook:
I had this uneasy feeling of “I’m not adequate,” until finally I said to myself, “I’m
going to stop, and read one sentence slowly, so I can figure out what the hell it
means.”
So I stopped—at random—and read the next sentence very carefully. I can’t
remember it precisely, but it was very close to this: “The individual member of the
social community often receives his information via visual, symbolic channels.” I
went back and forth over it, and translated. You know what it means? “People
read.”
17
Although Feynman’s method was aimed more at illustrating the deliberately
confusing nature of the prose rather than trying to understand a nuanced
meaning, the same method can help whenever you’re learning anything that goes
over your head.
I used this technique when taking a class on machine vision during the MIT
Challenge. I didn’t understand photogrammetry, a technique of determining the
3D shape of an object based on a series of 2D pictures taken under different
lighting conditions. It involved some tricky concepts, so I wasn’t quite sure how
it worked. With my textbook at my side, I wrote a few pages of notes, trying to
sketch out the broad strokes of the idea so I could get the general gist of it.
18
Application 2: For Problems You Can’t Seem to Solve
A second way to apply this is for solving a difficult problem or mastering a
technique. In this instance, it’s very important to go through the problem step by
step alongside the explanation you generate, rather than simply summarizing it.
Summarizing may end up skipping over the core difficulties of the problem.
Going deeper may take time, but it can help you get a strong grasp over a new
method in one go, rather than needing numerous repetitions to memorize the
steps.
I applied this to a class in computer graphics for a technique I was struggling
with called grid acceleration. This is a method of speeding up the performance of
ray-traced rendering systems by avoiding analyzing objects that “obviously”
won’t be on the part of the screen you’re drawing. To get a better handle on this,
I walked through the problem with the technique, drawing a little snowman that I
imagined rendering, with lines shooting out of an eyeball representing the
camera.
19
Application 3: For Expanding Your Intuition
A final way to apply this method is to ideas that are so important that it would
really help if you had a great intuition about them. In this application of the
method, instead of focusing on explaining every detail or going along with the
source material, you should try to focus on generating illustrative examples,
analogies, or visualizations that would make the idea comprehensible to
someone who has learned far less than you have. Imagine that instead of trying
to teach the idea, you are being paid to write a magazine article explaining the
idea. What visual intuitions would you use to pin down the abstractions? Which
examples would flesh out a general principle? How could you make something
confusing feel obvious?
I applied this to understanding the concept of voltage in an early class on
electromagnetism during the MIT Challenge. Though I was comfortable using
the concept in problems, I didn’t feel that I had a good intuition of what it was.
It’s obviously not energy, electrons, or flows of things. Still, it was hard to get a
mental image of an abstract concept on a wire. Going through this technique and
comparing the equations to the ones for gravity, it’s clear that voltage is to the
electrical force as height is to the gravitational force. Now I could form a visual
image. The wires were like troughs of water at different heights. Batteries were
like pumps, moving the water up. Resistors were like hoses dropping down, of
various widths to impede the flow of water draining down. Although this picture
of troughs and hoses wasn’t necessary for solving the equations, it stuck with me
and helped me reason my way out of new situations more easily than if voltage
had just been an abstract quantity.
Demystifying Intuition
When many people look at a genius like Richard Feynman, they’re inclined to
focus on his seemingly effortless intuitive leaps. In his playful style and
rebellious impulses, he may seem to defy the stereotype that learning requires
hard work. However, as we go beneath the surface, it becomes clear that he
shared much in common with the other ultralearners I’ve studied. He worked
hard on understanding things, and he put incredible amounts of his spare time
into mastering the methods that made his intuition work. In his early days in
college, he and a friend went back and forth over the early books on quantum
mechanics, racing ahead of their classmates to understand it. He even made a
meticulous timetable to allocate hours to his many intellectual pursuits. Even in
his trivial obsessions, he displayed a streak for aggressive methods; while
learning lock picking, for example, he trained himself to go through all the
possible combinations, practicing them repeatedly: “I got it down to an absolute
rhythm so I could try the 400 possible back numbers in less than half an hour.
That meant I could open a safe in a maximum of eight hours—with an average
time of four hours.”
20
When people hear about geniuses, especially the iconoclastic ones such as
Feynman, there’s a tendency to focus on their gifts and not their efforts. I have
no doubt that Feynman possessed gifts. But perhaps his greatest one was his
ability to merge tenacious practice and play. He approached picking locks with
the same enthusiasm for solving puzzles that he did for unraveling the secrets of
quantum electrodynamics. It’s this spirit of playful exploration that I want to
turn to in the final principle of ultralearning: experimentation.
Chapter XII
Principle 9
Experimentation
Explore Outside Your Comfort Zone
Results? Why, I have gotten lots of results! I know several thousand things that won’t
work.
—Thomas Edison
If you were to read his story without seeing his art, Vincent van Gogh would be
the last person you would expect to become one of the most famous painters of
all time. He started at a late age, twenty-six. Art is a field of precocity, and
famous masters typically display their gifts early. Pablo Picasso’s cubist style,
for example, came out of his already being able to paint realistically as a child,
allowing him to boldly declare that it had taken him “four years to paint as
Rafael, but a lifetime to paint like a child.” Leonardo da Vinci was apprenticed
as a painter in his teens. One story has him, as a youth, painting a monster on a
peasant’s shield only to have it resold to the duke of Milan. Salvador Dalí had
his first exhibit before his fourteenth birthday, already showcasing the talent that
would make him famous. Van Gogh, in contrast, was delayed and did not
possess any obvious signs of ability. It was only after failing as an art dealer and
minister that he picked up the brush. An art seller and family friend, H. G.
Tersteeg, believed his artistic aspirations were put on to mask his laziness. “You
started too late,” he declared. “Of one thing I am sure, you are no artist. . . . This
painting of yours will be like all the other things you started, it will come to
nothing.”
1
Worse than the fact that he started late, however, was that van Gogh simply
wasn’t very good at drawing. His drafting was crude and childlike. When he
finally convinced models to sit for portraits—no small task in light of the
Dutchman’s famously difficult personality—it took him many attempts to get
anything resembling a likeness. While in a brief stint at a Parisian atelier, he
even studied next to future leaders of the Post-Impressionist movement, such as
Henri de Toulouse-Lautrec. However, unlike the effortless quality with which
Toulouse-Lautrec captured the likeness of a scene with a few flicks of his wrist,
van Gogh struggled. “We considered his work too unskillful,” one classmate
recalled. “His drawings had nothing remarkable about them.”
2
In the end, his
inability to fit in with his classmates, lack of talent, and off-putting manners had
him leaving the studio after less than three months.
His late start and lack of obvious talent were compounded by his
temperament. Nearly everyone who entered his life would eventually reject him,
as his manic enthusiasm and fraternal solidarity would inevitably sour into bitter
fights with nearly every person he met. Near the end of his life, he was routinely
placed in mental asylums, varyingly diagnosed with disorders from “acute mania
with generalized delirium” to “a kind of epilepsy.” His outbursts, or “attacks,” as
he referred to them, alienated him from people who could potentially serve as his
peers, mentors, and teachers. As a result, despite having attempted formal
schooling, van Gogh was largely self-taught, capturing only brief moments of
more traditional education in the moments during which he could hold on to
friendships before pushing people away.
It was van Gogh’s mysterious and untimely death that cut short the artistic
career that was so late to begin. At thirty-seven, he died of a bullet wound to the
stomach. Although his death was suspected to be a suicide, his biographers
Steven Naifeh and Gregory White Smith consider accident or foul play more
likely; he may possibly even have been shot by one of the village youths who
played pranks on him and called him the fou roux, or “crazed redhead.”
In spite of all this, van Gogh has become one of the most famous painters of
all time. The Starry Night, Irises, and Vase with Fifteen Sunflowers have become
icons. On four separate occasions, a van Gogh piece has become the most
expensive painting ever sold, including his Portrait of Dr. Gachet, which was
sold for more than $82 million.
3 Van Gogh’s signature swirls of color, thick
impasto application, and strong outlines have made many people consider his
paintings some of the greatest of all time.
How can we explain these discrepancies? How does someone who starts late,
with no obvious talent and many handicaps, nonetheless become one of the
world’s greatest artists, with one of the most recognizable and distinctive styles?
To understand van Gogh, I want to turn to the ninth and final principle of
ultralearning: experimentation.
How van Gogh Learned to Paint
Put yourself in van Gogh’s shoes for a moment. You’ve failed miserably as an
art dealer, despite your family connections. You’ve failed as a preacher. Now
you’re embarking on a new profession—painting—even though you have
difficulties in drawing things accurately. What would you do? Van Gogh’s
response to this challenge was a pattern that would repeat throughout his life.
First, he would identify a learning resource, method, or style and pursue it with
incredible vigor, creating dozens, if not hundreds, of works in that direction.
After this burst of intensity, aware of his still-existing deficiencies, he would
apply himself to a new resource, method, or style and start again. Although
there’s no evidence van Gogh thought of the connection, I see a parallel between
this pattern and the one used by successful scientists: hypothesis, experiment,
results, repeat. Perhaps inadvertently, van Gogh’s aggressive, experimental
strides into painting allowed him to mature into not merely a proficient painter
but an unforgettably unique one.
Van Gogh’s experimentation began when he was first trying to become an
artist. The normal route to an artistic career in those days was to attend an art
school or apprentice in a studio. Van Gogh, due to the fact that others did not see
him as possessing much talent and his odd temperament, did not have much luck
with those traditional avenues. Therefore he turned to self-education, pursuing
home-study courses that promised to teach him the basics of drawing. In
particular, he made heavy use of Charles Bargue’s Exercices au fusain (Charcoal
Exercises) and Cours de dessin (Drawing Course), as well as Armand
Cassagne’s Guide de l’alphabet du dessin (Guide to the ABCs of Drawing).
They were thick books with graduated exercises on which aspiring artists could
work step by step to improve their drawing skill. According to his biographers,
van Gogh “devoured these big books . . . page by page, over and over.” Van
Gogh himself reported to his brother, Theo, “I have now finished all sixty
sheets,” adding “I worked almost a whole fortnight, from early morning until
night.”
4 Copying was another strategy van Gogh employed early on that he
would continue late into his artistic career. Jean-François Millet’s The Sower
was one of his favorite pictures to copy, which he did again and again. He also
applied himself to sketching from life early, in particular models for portraits,
which he struggled with due to his difficulties with drafting accurately.
Van Gogh studied from other artists, friends, and mentors. Anthon van
Rappard convinced him to try out reed pen and ink and adopt the mature artist’s
style of short and fast strokes. Another artist, Anton Mauve, persuaded him to try
a variety of different media: charcoal and chalk, watercolor, and Conté crayon.
Often those attempts were not successful. During their stay together at the house
where van Gogh would later cut off his ear, Paul Gauguin pushed the Dutchman
to paint from memory, mute his colors, and adopt new materials for different
effects. Those tactics didn’t work for van Gogh, whose weaknesses in drafting
were exacerbated by not having the scene directly in front of him, and the
different materials went against the style that would later make him famous.
Experiments, however, needn’t always be successful to have value, and van
Gogh had many opportunities for trying new techniques.
Van Gogh experimented not just with materials and methods but also with the
philosophies that underpinned his art. Although he is most famous for strong,
vibrant colors, that wasn’t his initial intention. Originally, he leaned toward the
profundity of muted, grayer tones, as witnessed in an early work The Potato
Eaters. “Scarcely any color is not gray,” he argued. “In nature one really sees
nothing else but those tones or shades.”
5 He was fully convinced of that and
based his work on it accordingly. However, he would later switch to the exact
opposite: bright, complementary colors, often imposed on a scene rather than
being brought out from nature. His stance on contemporary artistic movements
flitted about; at first he preferred traditional painting to the new Impressionist
style, and later he shifted to the avant-garde, opting for bold forms rather than
verisimilitude.
There are two important things to note about van Gogh’s experiments in art.
The first is the variety of methods, ideas, and resources he applied. Since he
struggled with many aspects of painting, I believe that variation was important to
his eventually finding a style that would work for him—one that would take
advantage of his strengths and diminish the significance of his weaknesses.
Although virtuoso talents might be able to latch on to the first style of instruction
Although virtuoso talents might be able to latch on to the first style of instruction
they are presented with and follow it to completion, others require a great deal of
experimentation before the right method sticks. The second important thing to
note is his intensity. Like all the ultralearners I’ve discussed so far, van Gogh
was tenacious in his efforts to become an artist. Despite receiving much negative
feedback and discouragement, he pursued his art relentlessly, sometimes
producing as much as a new painting every day. These two factors, variation and
aggressive exploration, enabled him to push through his early obstacles and
produce some of the most iconic and brilliant works ever painted.
Experimentation Is the Key to Mastery
When starting to learn a new skill, often it’s sufficient simply to follow the
example of someone who is further along than you. In discussing the principles
of ultralearning, metalearning comes first. Understanding how a subject breaks
down into different elements and seeing how others have learned it previously,
thus providing an advantageous starting point. However, as your skill develops,
it’s often no longer enough to simply follow the examples of others; you need to
experiment and find your own path.
Part of the reason for this is that the early part of learning a skill tends to be
the best trodden and supported, as everyone begins at the same place. As your
skills develop, however, not only are there fewer people who can teach you and
fewer students you could have as peers (thus lowering the total market for books,
classes, and instructors), but you also start to diverge from those you’re learning
from. Whereas two complete novices have quite similar knowledge and skills,
two experts might have quite different sets of skills that they’ve already
acquired, thus making improving those skills an increasingly personalized and
idiosyncratic adventure.
A second reason for the value of experimentation as you approach mastery is
that abilities are more likely to stagnate after you’ve mastered the basics.
Learning in the early phases of a skill is an act of accumulation. You acquire
new facts, knowledge, and skills to handle problems you didn’t know how to
solve before. Getting better, however, increasingly becomes an act of
unlearning; not only must you learn to solve problems you couldn’t before, you
must unlearn stale and ineffective approaches for solving those problems. The
difference between a novice programmer and a master isn’t usually that the
novice cannot solve certain problems. Rather, it’s that the master knows the best
novice cannot solve certain problems. Rather, it’s that the master knows the best
way to solve a problem, which will be the most efficient and clean and cause the
fewest headaches later on. As mastery becomes a process of unlearning over
accumulation, experimentation becomes synonymous with learning as you force
yourself to go outside your comfort zone and try new things.
A final reason for the increasing importance of experimentation as you
approach mastery is that many skills reward not only proficiency but originality.
A great mathematician is one who can solve problems others cannot, not merely
a person who can solve previously solved problems easily. Successful business
leaders are those who can spot opportunities others cannot, not merely those who
can copy the style and strategy of those before them. In art, it was not only van
Gogh’s skill but his originality that made him one of the most celebrated painters
to have ever lived. As creativity becomes valuable, experimentation becomes
essential.
Three Types of Experimentation
In experimentation you can see different levels play out, both in van Gogh’s path
as an artist and as a model for your own explorations:
1. Experimenting with Learning Resources
The first place to experiment is with the methods, materials, and resources you
use to learn. Van Gogh did this extensively at the beginning of his artistic career,
trying out different artistic media, materials, and learning techniques: following
home-study courses, watching fellow artists, sketching from life and in the
studio, and more. This kind of experimentation is useful in helping you discover
the guides and resources that work best for you. It’s important, however, that
your impulse to experiment be matched with a drive to do the necessary work.
Although van Gogh tried many different approaches when he first started
teaching himself to draw and paint, he also produced an enormous quantity of
work based on each of those methods.
A good strategy to take is to pick a resource (maybe a book, class, or method
of learning) and apply it rigorously for a predetermined period of time. Once you
apply yourself aggressively to that new method, you can step back and evaluate
how well it is working and whether you feel it makes sense to continue with that
approach or try another.
approach or try another.
2. Experimenting with Technique
In the beginning, experimentation tends to focus on materials. However, in most
domains of learning, the options for what to learn next expand faster and faster,
so the question becomes not “How can I learn this?” but “What should I learn
next?” Languages are a prime example. The same basic set of vocabulary and
phrases dominates most beginner resources. As you improve, however, the
amount of things you could possibly learn next becomes larger and larger.
Should you learn to read literature? Converse fluently on a professional topic?
Read comic books? Have business discussions? The specialized vocabulary,
phrases, and cultural knowledge in each field multiply, so it becomes necessary
to choose what to master.
Once again, experimentation plays a pivotal role. Pick some subtopic within
the skill you’re trying to cultivate, spend some time learning it aggressively, and
then evaluate your progress. Should you continue in that direction or pick
another? There’s no “right” answer here, but there are answers that will be more
useful to the specific skill you’re trying to master.
3. Experimenting with Style
After you’ve matured in your learning a bit, the difficulty often switches from
which resources to learn from or which techniques you’d like to master to the
style you’d like to cultivate. Although there are some skills that have one and
only one “correct” way of doing things, this is not true of most. Writing, design,
leadership, music, art, and research all involve developing certain styles, which
have different trade-offs. Once you master the basics, there is no longer one
“right” way to do everything but many different possibilities, all of which have
different strengths and weaknesses. This affords another opportunity for
experimentation. Van Gogh tried out many different styles for producing art,
varying from those of traditional painters such as Millet, to Japanese woodblock
prints, and studied the techniques used by his artist friends such as Gauguin and
Rappard. There is no one correct answer, although, like van Gogh, you may find
that certain styles work better than others with your unique combination of
strengths and weaknesses.
The key to experimenting with different styles is to be aware of all the
The key to experimenting with different styles is to be aware of all the
different styles that exist. Once again van Gogh provides a good model, as he
spent an enormous amount of time studying and discussing the works of other
artists. That gave him a large library of possible styles and ideas he could adapt
to his own work. Similarly, you might want to identify masters in your own line
of study and dissect what makes their styles successful to see what you can
emulate or integrate into your own approach.
In each level of experimentation, the choices broaden and the possible options to
explore go up exponentially. There’s a tension, therefore, between spending time
trying out different resources, techniques, and styles, and concentrating your
efforts on a single approach long enough to become proficient at it. This tension
often resolves itself as you cycle through exploring a new avenue in learning and
then buckling down to learn it deeply before moving on to something else.
Whatever else his failings, it was this pattern of trying out an idea and working
on it aggressively that van Gogh applied brilliantly.
The Mindset of Experimentation
There are parallels between the mindset required to experiment and what the
Stanford psychologist Carol Dweck refers to as growth mindset.
6
In her research,
she distinguishes between two different ways of looking at one’s own learning
and potential. In a fixed mindset, learners believe that their traits are fixed or
innate and thus there’s no point in trying to improve them. In a growth mindset,
in contrast, learners see their own capacity for learning as something that can be
actively improved. In some ways, these two types of mindsets become selffulfilling prophecies. Those who think they can improve and grow, do; those
who think they are fixed and immutable remain stuck.
The parallel with the mindset required for experimentation is clear.
Experimenting is based on the belief that improvements are possible in how you
approach your work. If you think your learning styles are fixed or that you have
certain immutable strengths and weaknesses that will keep you from trying out
different ways to approach your skills, you won’t be able to experiment at all.
I see the experimental mindset as an extension of the growth mindset: whereas
the growth mindset encourages you to see opportunities and potential for
improvement, experimentation enacts a plan to reach those improvements. The
experimental mindset doesn’t just assume that growth is possible but creates an
active strategy for exploring all the possible ways to reach it. To get into the
active strategy for exploring all the possible ways to reach it. To get into the
right mental space for experimenting, you need not only to see your abilities as
something you can improve but understand that there is a huge number of
potential avenues to do this. Exploration, not dogmatism, is the key to realizing
that potential.
How to Experiment
Experimenting sounds simple but can be quite tricky to implement in practice.
The reason is that a flurry of random activity doesn’t usually translate into
mastery. In order to work, experimenting requires understanding what learning
problems you’re facing and coming up with possible ways to resolve them. Here
are a few tactics that can help you integrate experimentation into your
ultralearning projects.
Tactic 1: Copy, Then Create
This is the first strategy for experimenting, which we can see in van Gogh’s
work. Though van Gogh is best known for his original pieces, he also spent a lot
of time copying drawings and paintings he liked by other artists. Copying
simplifies the problem of experimentation somewhat because it gives you a
starting point for making decisions. If you’re learning to paint, as van Gogh did,
the possibilities of what kinds of art you can create and techniques you can apply
are so vast that it can be difficult or impossible to decide among them. However,
if you start by emulating another artist, you can use that foothold to venture
further in your own creative directions.
This strategy has another advantage beyond simplifying the choices available
to you. In attempting to emulate or copy an example you appreciate, you must
deconstruct it to understand why it works. This can often highlight things that
the other person does exceptionally well that weren’t obvious from the
beginning. It may also dispel illusions you may have had about an aspect of the
work you thought was important but upon emulating the other person’s work
you realize was not.
7
Tactic 2: Compare Methods Side-by-Side
The scientific method works by carefully controlling conditions so that the
difference between two situations is limited to the variable being studied. You
can apply this same process to your experiments in learning by trying two
different approaches and varying only a single condition to see what the impact
is. By applying two different approaches side by side, you can often quickly get
information not only about what works best but about which methods are better
suited to your personal style.
I applied this to learning French vocabulary. I wasn’t sure how effective
mnemonics would be so, for a month, I would find a list of fifty new words
every day, put together from my regular reading or random encounters with the
language, and for half I would simply look them over with their translations I got
from the dictionary. With the other half, I made an effort to use a visual
mnemonic to link the two meanings. Then I compared how many of the words I
remembered from each list on a later test, with words picked randomly from
each side. The result is something you would probably expect after reading the
chapters on retrieval and retention: I remembered the words I used mnemonics
for at almost twice the rate of those I didn’t. That showed that even if creating
the mnemonics took a bit more time, they were worth it.
There are two advantages to doing split tests. The first is that as in scientific
experiments, you will get much better information about which method works
best if you limit the variation to only the factor you want to test. The second is
that by solving a problem multiple ways or applying multiple solution styles to
it, you will increase your breadth of expertise. Forcing yourself to try different
approaches encourages experimentation outside your comfort zone.
Tactic 3: Introduce New Constraints
The challenge of learning in the beginning is that you don’t know what to do.
The challenge of learning in the end is that you think you already know what to
do. It’s this latter difficulty that causes us to rerun old routines and old ways of
solving problems that are encouraged through habit, not always because the old
way is actually best. A powerful technique for pushing out of those grooves of
routine is by introducing new constraints that make the old methods impossible
to use.
It’s practically an axiom of design that the best innovations come from
working within constraints. Give a designer unlimited freedom, and the solution
is usually a mess. On the other hand, creating specific constraints in how you can
proceed encourages you to explore options that are less familiar to you and
proceed encourages you to explore options that are less familiar to you and
sharpens your underlying skills. How can you add limitations to force yourself to
develop new capacities?
Tactic 4: Find Your Superpower in the Hybrid of
Unrelated Skills
The traditional path to mastery is to take a well-defined skill and practice it
relentlessly until you have become insanely good at it. This is the path taken by
many athletes, who train for decades to perfect their shot, jump, kick, or throw.
However, for many areas of creative or professional skills, another, more
accessible, path is to combine two skills that don’t necessarily overlap to bring
about a distinct advantage that those who specialize in only one of those skills do
not have. For instance, you might be an engineer who becomes really good at
public speaking. You may not be the best possible engineer or the best possible
presenter, but combining those two skills could make you the best person to
present on engineering topics for your company at conferences, thus giving you
access to new professional opportunities. Scott Adams, the creator of Dilbert,
likened his own success to following this strategy by combining his background
as an engineer with an MBA and a cartoonist.
8
This level of experimentation often plays out over multiple ultralearning
projects. After I completed my MIT Challenge, I could apply the programming
knowledge I had obtained to write scripts to automatically generate flash cards
for learning Chinese. Such synergies become possible once you start exploring
how one skill you’ve already acquired can impact another.
Tactic 5: Explore the Extremes
Van Gogh’s art pushed well outside normal conventions along many
dimensions. His thick application of paint was far away from the thin layers of
glazes used by Renaissance masters. His quick application was far more rapid
than the careful brushstrokes of other painters. His colors were bold, often
garish, instead of subtle. If you were to draw a chart that mapped out van Gogh’s
style compared with those of other painters, you would probably see that he lay
along the extreme in many dimensions.
An interesting result from mathematics is that as you get to higher and higher
dimensions, most of the volume of a higher-dimensional sphere lies near its
dimensions, most of the volume of a higher-dimensional sphere lies near its
surface. For instance, in two dimensions (a circle), just under 20 percent of its
mass lies in the outer shell described by a tenth of the radius. In three dimensions
(a sphere), that number rises to almost 30 percent. In ten dimensions, almost
three-quarters of the mass is in that outermost layer. You can imagine learning a
complex subject as akin to trying to find an optimal point in a region of higherdimensional space—except that instead of length, width, and height, those
dimensions might be the qualitative dimensions of work, such as van Gogh’s
complementarity of colors, application of paint, or some other aspect of skill that
can be applied in some varying degree of intensity. What this means is that the
more complicated a domain of skill is (i.e., the more dimensions it contains), the
more space will be taken up by applications of that skill that are extreme across
at least one of those dimensions. This suggests that for many skills, the best
option is going to be extreme in some way, since so many more of the
possibilities are themselves extreme. Sticking to the middle and playing it safe
isn’t the correct approach because that allows you to explore only a small subset
of the total possibilities for your work.
Pushing out to an extreme in some aspect of the skill you’re cultivating, even
if you eventually decide to pull it back to something more moderate, is often a
good exploration strategy. This allows you to search the space of possibilities
more effectively, while also giving you a broader range of experience.
Experimentation and Uncertainty
Learning is a process of experimenting in two ways. First, the act of learning
itself is a kind of trial and error. Practicing directly, getting feedback, and trying
to summon up the right answers to problems are all ways of adjusting the
knowledge and skills you have in your head to the real world. Second, the act of
experimenting also lies in the process of trying out your learning methods. Try
out different approaches, and use the ones that work best for you. The principles
I’ve tried to articulate in this book should provide good starting points. But they
are guidelines, not iron rules; starting points, not destinations. Only by
experimenting will you be able to find the right trade-offs between different
principles—for instance, when directness is more important and when you
should focus on drills or whether retention or intuition is the main obstacle to
learning. Experimenting will also help you decide among small differences in
approach that no list of principles could possibly cover exhaustively.
approach that no list of principles could possibly cover exhaustively.
Having a mindset of experimentation will also encourage you to explore
beyond what you feel most comfortable doing. Many people stick to the same
routines, the same narrow set of methods, they apply to learning everything. As a
result, there are a lot of things they struggle to learn because they don’t know the
best way to do so. Copying exemplars, running tests, and pushing to extremes
are all ways to push outside your ingrained habits and try out something
different. That process will teach you not just abstract learning principles but
concrete tactics that will accommodate your personality, interests, strengths, and
weaknesses. Are you better off learning a language through practicing speaking
or engaging in lots of input through movies and books? Are you better off
learning programming by building your own game or working on open-source
projects? These questions don’t have a single correct answer, and people have
achieved success using a wide variety of different methods.
My own experience with learning has been one of constant experimentation.
In university, I focused a lot on making associations and connections. During the
MIT Challenge, I switched to making practice the foundation. In my first
experience learning a language, I was sloppy, speaking English most of the time.
In the second round, I experimented with going to another extreme, to see if I
could avoid that sticking point. While doing projects, I’ve had to adjust my
methods frequently. Even though it was only thirty days long, my portraitdrawing challenge involved a lot of trial and error from starting by doing
sketches and, when my progress using that approach slowed, trying to do
sketches even faster to get more feedback. When that, too, had reached its limits,
I spent some time learning a different technique altogether to achieve greater
accuracy.
Embedded in my successes are many failures—times where I thought I could
get something to work and it ended up failing miserably. Early on in learning
Chinese, I thought I could use some kind of mnemonic system to remember the
words, with colors for tones and memorized symbols for the syllables. That was
because my normal sounds-like method for visual mnemonics wasn’t working
with the words, which all sounded so different from English. The result was a
total failure, and it didn’t work at all! Other times, my experiments with new
methods worked out great. Most of the techniques I’ve shared in this book thus
far started as ideas I wasn’t sure would pan out.
Experimentation is the principle that ties all the others together. Not only does
it make you try new things and think hard about how to solve specific learning
challenges, it also encourages you to be ruthless in discarding methods that don’t
challenges, it also encourages you to be ruthless in discarding methods that don’t
work. Careful experimentation not only brings out your best potential, it also
eliminates bad habits and superstitions by putting them to the test of real-world
results.
Chapter XIII
Your First Ultralearning Project
The beginning is always today.
—Mary Shelley
By now you’re probably eager to start your own ultralearning project. What
things could you learn that you have put off due to fears of inadequacy,
frustration, or lack of time? What old skills could you take to new heights? The
biggest obstacle to ultralearning is simply that most people don’t care enough
about their own self-education to get started. As you’ve read this far, I doubt that
is true of you. Learning, in whatever forms it takes, is something that’s important
to you. The question is whether that spark of interest will ignite into a flame or
be smothered prematurely.
Ultralearning projects aren’t easy. They require planning, time, and effort. Yet
the rewards are worth the effort. Being able to learn hard things quickly and
effectively is a powerful skill. One successful project tends to lead to others. It’s
usually the first project that requires the most thought and care. A solid, wellresearched, well-executed plan can give you the confidence to face harder
challenges in the future. A bungled attempt is not a disaster, but it may make you
reluctant to pursue future projects of a similar nature. In this chapter, I’d like to
tell you everything I’ve learned about how to get it right.
Step 1: Do Your Research
The first step in any project is to do the metalearning research required to give
you a good starting point. Planning ahead will avoid a lot of problems and
prevent you from having to make drastic changes to your learning plan before
you’ve even started making progress. Research is a bit like packing a suitcase for
a long voyage. You may not bring the right items, or you may forget something
and need to buy it on the road. However, thinking ahead and packing your bags
correctly will prevent a lot of fumbling later. Your ultralearning “packing”
checklist should include, at a minimum:
1. WHAT TOPIC YOU’RE GOING TO LEARN AND ITS APPROXIMATE SCOPE.
Obviously, no learning project can begin unless you figure out what you
want to learn. In some cases, this is obvious. In others, you may need to do
further research to identify which skill or knowledge would be most
valuable. If your goal is to learn something instrumentally (to start a
business, get a promotion, do research for an article), learning what you
need to learn is important and will suggest how wide and deep you need to
go. I suggest starting with rather a narrow scope, which can expand as you
proceed. “Learning enough Mandarin Chinese to hold a fifteen-minute
conversation on simple topics” is a lot more constrained than “Learn
Chinese,” which may include reading, writing, studying history, and more.
2. THE PRIMARY RESOURCES YOU’RE GOING TO USE. This includes books,
videos, classes, tutorials, guides, and even people who will serve as
mentors, coaches, and peers. This is where you decide what your starting
point will be. Examples: “I’m going to read and complete the exercises in a
book on Python programming for beginners” or “I’m going to learn Spanish
through online tutoring via italki.com” or “I’m going to practice drawing by
making sketches.” In some subjects, static materials will determine how
you proceed. In others, they will be supports to back up your practice. In
any case, they should be identified, purchased, borrowed, or enrolled in
before you begin.
3. A BENCHMARK FOR HOW OTHERS HAVE SUCCESSFULLY LEARNED
THIS SKILL OR SUBJECT. Almost any popular skill has online forums
where those who have learned the skill previously can share their
approaches. You should identify the things other people who have learned
the skill have done to learn it. This doesn’t mean you need to follow exactly
in their footsteps, but it will prevent you from completely missing
something important. The Expert Interview Method in chapter 4 provides a
good method for following up on this.
4. DIRECT PRACTICE ACTIVITIES. Every skill and subject you’re learning
will be used somewhere eventually, even if it’s as simple as using it to learn
something else. Thinking about how you might use the skill can enable you
to start finding opportunities to practice it as early as possible. If direct
practice is impossible, you should nonetheless identify opportunities for
practice that mimic the mental requirements of using the skill.
5. BACKUP MATERIALS AND DRILLS. In addition to the principal materials
and methods you’ll be using, it’s a good idea to look at possible drills and
backup materials you may want to use. Backup materials are often good if
you recognize that a certain tool or set of materials might be useful but you
don’t want to be overwhelmed in the beginning.
Step 2: Schedule Your Time
Your ultralearning project doesn’t need to be an intensive, full-time endeavor to
succeed. However, it will require some time investment, and it’s better to decide
on how much time you are willing to devote to learning in advance than simply
hope that you’ll find the time later. There are two good reasons for planning
your schedule ahead of time. The first is that this way you subconsciously
prioritize your project by setting it down on your calendar ahead of other things.
The second is that learning is often frustrating and it is almost always easier to
click over to Facebook, Twitter, or Netflix. If you don’t set aside time to learn, it
will be a lot harder to summon up the motivation to do so.
The first decision you should make is how much time you’re going to commit.
This is often dictated by your schedule. You may have a gap in employment that
allows intensive learning, but only for a month. Alternatively, you may have a
full schedule that permits you to devote only a few hours per week to learning
something new. Whatever time you can commit, decide on it in advance.
The second decision you need to make is when you are going to learn. During
a few hours on Sunday? By waking up an hour earlier and putting in the time
before work? In the evening? During lunch breaks? Once again, the best thing is
to do whatever makes it easiest based on your schedule. I recommend setting a
to do whatever makes it easiest based on your schedule. I recommend setting a
consistent schedule that is the same every week, rather than trying to fit in
learning when you can. Consistency breeds good habits, reducing the effort
required to study. If you have absolutely no choice, an ad hoc schedule is better
than none, but it will require more discipline to sustain.
If you do have some flexibility in your schedule, you may want to optimize it.
Shorter, spaced time chunks are better for memory than crammed chunks are.
However, some types of tasks, such as writing and programming, have a long
warm-up time that may benefit from longer uninterrupted time chunks. The best
way to find out what is best for you is to practice; if you find it takes a long time
to warm up, opt for longer spaces in your schedule. If you find you can get to
work within a few minutes of starting, shorter chunks of time spread out will be
helpful for long-term retention.
The third decision you need to make is the length of time for your project. I
generally prefer shorter commitments to longer ones because they are easier to
stick with. An intensive project that lasts a month has fewer potential
interruptions from life or from your motivation changing and waning. If you
have a big goal you want to accomplish that can’t be done in a short time frame,
I suggest breaking it up into multiple smaller ones of a few months each.
Finally, take all this information and put it into your calendar. Scheduling all
the hours of work on the project in advance has important logistical and
psychological benefits. Logistically, this will help you spot potential conflicts in
your schedule due to vacations, work, or family events. Psychologically, it will
help you remember and act on your initial plan better than if it were written on a
piece of paper tucked into a desk drawer. What’s more, the act of scheduling
demonstrates your seriousness about doing the project.
I can clearly remember writing down my hypothetical studying schedule
before starting the MIT Challenge. It had me up and studying by 7 a.m. and
working until 6 p.m., with only a short break for lunch. Although my actual
schedule, in practice, rarely reached that ideal (even in my most intensive early
days, I almost never got in eleven hours straight), the mere act of writing down
the schedule helped prepare me psychologically for the project ahead. If you’re
unwilling to put time into your calendar, you’re almost certainly unwilling to put
in time to study. If you’re waffling at this stage, that’s a good sign your heart
isn’t really in the right place to get started.
As a bonus step, for those who are embarking on longer projects of six months
or more, I strongly recommend doing a pilot week of your schedule. This is
simple: test your schedule for one week before you commit to it. This will give
you firsthand knowledge of how difficult it will be and prevents overconfidence.
you firsthand knowledge of how difficult it will be and prevents overconfidence.
If you already feel burned out after the first week, you may need to make
adjustments. There’s no shame in going back and retooling your plan to make it
fit your life better. Making this kind of adjustment is a lot better than giving up
midway because your plan was doomed from the start.
Step 3: Execute Your Plan
Whatever plan you started with, now is the time to do it. No plan is perfect, and
you may realize that what you’re doing for learning departs from the ideal, as
established by the ultralearning principles. You may notice that your plan relies
too much on passive reading rather than retrieval practice. You may see that the
way you’re practicing is a winding detour away from where you will actually
want to use it. You may feel as though you’re forgetting things or memorizing
them without really understanding them. That’s okay. In some cases, you won’t
be able to have the perfect learning approach because the resources to do so
don’t exist. However, becoming sensitive to how the way you’re learning isn’t
aligned with the principles is a good way to feel out changes you can make to
improve it.
Here are some questions to ask yourself to determine whether you’re slipping
from the ideal:
1. METALEARNING. Have I done research into what are the typical ways of
learning this subject or skill? Have I interviewed successful learners to see
what resources and advice they can recommend? Have I spent about 10
percent of the total time on preparing my project?
2. FOCUS. Am I focused when I spend time learning, or am I multitasking and
distracted? Am I skipping learning sessions or procrastinating? When I start
a session, how long does it take before I’m in a good flow? How long can I
sustain that focus before my mind starts to wander? How sharp is my
attention? Should it be more concentrated for intensity or more diffuse for
creativity?
3. DIRECTNESS. Am I learning the skill in the way I’ll eventually be using it?
If not, what mental processes are missing from my practice that exist in the
real environment? How can I practice transferring the knowledge I learn
from my book/class/video to real life?
4. DRILL. Am I spending time focusing on the weakest points of my
performance? What is the rate-limiting step that is holding me back? Does
it feel as though my learning is slowing down and that there’s too many
components of the skill to master? If so, how can I split apart a complex
skill to work on smaller, more manageable components of it?
5. RETRIEVAL. Am I spending most of my time reading and reviewing, or am
I solving problems and recalling things from memory without looking at
my notes? Do I have some way of testing myself, or do I just assume I’ll
remember? Can I successfully explain what I learned yesterday, last week,
a year ago? How do I know if I can?
6. FEEDBACK. Am I getting honest feedback about my performance early on,
or am I trying to dodge the punches and avoid criticism? Do I know what
I’m learning well and what I’m not? Am I using feedback correctly, or am I
overreacting to noisy data?
7. RETENTION. Do I have a plan in place to remember what I’m learning long
term? Am I spacing my exposure to information so it will stick longer? Am
I turning factual knowledge into procedures that I’ll retain? Am I
overlearning the most critical aspects of the skill?
8. INTUITION. Do I deeply understand the things I’m learning, or am I just
memorizing? Could I teach the ideas and procedures I’m studying to
someone else? Is it clear to me why what I’m learning is true, or does it all
seem arbitrary and unrelated?
9. EXPERIMENTATION. Am I getting stuck with my current resources and
techniques? Do I need to branch out and try new approaches to reach my
goal? How can I go beyond mastering the basics and create a unique style
to solve problems creatively and do things others haven’t explored before?
Together these principles serve as directions, not destinations. In each case,
look at how you’re currently progressing through your materials, and see what
you could do differently. Do you need to switch resources? Do you need to stick
to the same resources but spend more time on a different kind of practice?
Should you seek out new environments for feedback, directness, or immersion?
These are all subtle adjustments you can make along the way.
Step 4: Review Your Results
After your project is finished (or if you end up putting it on pause for some
reason), you should spend a little time analyzing it. What went right? What went
wrong? What should you do next time to avoid making those same mistakes?
Not all of your projects will be successful. I’ve had ultralearning projects that
I felt good about. I’ve had others that didn’t work out as well as I had hoped.
Although the tendency is to blame willpower and motivation, very often the
problems with projects can be traced back to their conception. I worked on one
project devoted to improving my Korean, after my trip, by investing five hours
per week. It wasn’t as successful as I had hoped because I didn’t invest enough
time in focusing on immersive, direct practice from the get-go. Instead, my study
method depended a lot on textbook exercises, which were boring and didn’t
transfer too well to the real world. If I had thought a little more about it, I would
have spent a week or two ahead of time trying to find places to practice, instead
of trying to pivot midway, when I was already losing some motivation. This
struggle illustrates that mastering the principles is a lifelong process. Even after
many experiences learning languages, and knowing what works well, I slipped
into a less effective approach because I didn’t plan my project adequately. In
other cases a project might not work out as you had hoped, but that lesson will
still be valuable. I started with a project to learn cognitive science more deeply,
going from a book list. Eventually, however, a lot of that project morphed into a
desire to do research for this book, which put me into contact with a lot of
science, now combined with an outlet for a more direct way to apply it.
Even your successful projects are worth analyzing. They can often tell you
more than your failures because the reasons a successful project succeeded are
the very elements you want to retain and replicate for the future. With
ultralearning, as with all self-education, the goal isn’t merely to learn one skill or
subject but to hone and enhance your overall learning process. Each successful
project can be refined and improved for the next one.
Step 5: Choose to Maintain or Master What You’ve
Learned
After you’ve learned your skill and analyzed your efforts, you have a choice to
make. What do you want to do with the skill? With no plan in place, most
make. What do you want to do with the skill? With no plan in place, most
knowledge eventually decays. This can be alleviated somewhat by following the
principles of ultralearning. However, all knowledge decays without any form of
intervention, so the best time to make a choice about how you’re going to handle
that is right after you learn something.
Option 1: Maintenance
The first option is to invest enough practice to sustain the skill but without any
concrete goal of getting it to a new level. This can often be accomplished by
setting up a habit of regular practice, even if it is a minimal one. As mentioned in
the chapter on retention, one of the worries I had after the year without English
project was that learning languages so intensively over a short period of time
might lead not just to rapid learning but to rapid forgetting. As a result, I made
an effort to continue practice after the trip finished, spending thirty minutes a
week on each language in the first year and thirty minutes a month on each
language in the year after that.
Another option is to try to integrate the skill into your life. This is how I
maintain my programming skills, where I write Python scripts to handle work
tasks that would otherwise be cumbersome or annoying. This kind of practice is
more sporadic, but it ensures that I will keep it up enough to make it usable. This
kind of lightweight usage is far from the deep math and algorithms I learned
from my MIT coursework, but it is enough to keep a foot in the door if I want to
embark on a bigger project at a later time.
Forgetting, as was discovered by Hermann Ebbinghaus more than a hundred
years ago, falls off with an exponentially decaying curve. That means that
memories that are retained for longer are less and less likely to be forgotten
when you follow up at a later date. This pattern suggests that maintenance
practice, too, can fall off on a decaying rate, so that the bulk of the knowledge
you’ve acquired will be preserved. This means you might want to start with a
habit of more serious practice but reduce the time spent on it a year or two after
your project is finished to still get most of the benefit, as I did with the languages
I studied.
Option 2: Relearning
Forgetting isn’t ideal, but for many skills the costs of relearning the skill later are
smaller than the costs of keeping it continuously sharp. There are a couple
smaller than the costs of keeping it continuously sharp. There are a couple
reasons for this. First, you may have learned more than you actually need, so if
some of that knowledge selectively decays due to disuse, it is automatically
going to be the less important knowledge that you acquired. I studied a lot of
MIT subjects that I don’t think I’ll ever use again, although understanding the
gist of them might come in handy later. Therefore, keeping my ability to prove
theorems of modal logic, for example, up to date has only marginal value.
Knowing what modal logic is and where I might apply it in case I want to learn
something that requires it is probably enough.
Relearning is generally easier than first-time learning. Although performance
on tests drops off dramatically, the knowledge is likely inaccessible rather than
completely forgotten. This means that doing a refresher course or practice series
can be enough to reactivate most of it in a fraction of the time it took to learn it
initially. This may be the optimal strategy for subjects that you need to use
infrequently and for which situations for using them won’t pop up without
warning. Often, recognizing that a certain domain of knowledge is helpful for a
particular problem type is more important than the details of solving the
problem, since the latter can be relearned but forgetting the former will cut you
off from solving those problems.
Option 3: Mastery
The third option, of course, is to dive deeper into the skill you have learned. This
can be done through continued practice at a lighter pace or by following up with
another ultralearning project. A common pattern I’ve noticed in my own learning
is that an initial project covers a wider territory and some basics and exposes
new avenues for learning that were previously obscured. You might identify a
subtopic or branch of skill within the domain you were learning before that you
want to follow up. Otherwise, you may decide to transfer a skill learned in one
place to a new domain. One of my goals after returning from my trip to China
was to learn to read Chinese better, which had been only an incidental goal while
I was traveling there.
Mastery is a long road that extends far beyond a single project. Sometimes the
barriers you overcome in your initial effort are enough to clear the way for a
slow process of accumulation to reach eventual mastery. In many domains,
getting started is quite frustrating, so it’s difficult to practice without a certain
amount of effort. After that threshold is reached, however, the process switches
to being one of accumulating huge swaths of knowledge and therefore can
to being one of accumulating huge swaths of knowledge and therefore can
proceed at a more patient pace. On the other hand, some projects will get stuck,
and you will need to spend time unlearning and push through your frustrations
again to get ahead. Those kinds of projects benefit more from the precise and
aggressive methods of ultralearning to reach eventual mastery.
Alternatives to Ultralearning: Low-Intensity Habits and
Formal Instruction
At the start of this book, I pointed out that ultralearning is a strategy. Being a
strategy implies that it is good for solving certain problems. Given that the
practice is somewhat uncommon, I wanted to spend the book focused on this
strategy, rather than try to give a diffuse description of all possible ways you can
learn effectively. However, now that I’ve done that, I think it’s worthwhile to
touch on two other strategies that can work with ultralearning, in different
contexts.
None of the ultralearners I encountered approaches learning the same way for
every kind of learning they do. Benny Lewis, for example, does do intensive
learning projects for languages, but he has learned most of his languages over
repeat visits to the countries they are spoken in, digging deeper into languages he
previously established in intense bursts. Roger Craig did learn aggressively to
win at Jeopardy!, but he also engaged in more leisurely acquisition of trivia
when his appearance on the game show wasn’t imminent. Being an ultralearner
doesn’t imply that everything one learns has to be done in the most aggressive
and dramatic fashion possible. I want to briefly consider the two main alternative
strategies to ultralearning to see how they fit into a bigger picture of lifelong
learning.
Alternative Strategy 1: Low-Intensity Habits
Low-intensity habits work well when engaging in learning is spontaneous, your
frustration level is low, and learning is automatically rewarding. In these cases,
when the barriers to learning are fairly low, all you need to do is show up. No
fancy project, principles, or effort is required. Once you reach a conversational
level in a language, for instance, it’s often fairly easy to travel and live in a
country where it is spoken, accumulating more and more vocabulary and
country where it is spoken, accumulating more and more vocabulary and
knowledge over a longer period of time. Similarly, once you become good
enough at programming to use it for your job, the job itself will push you to
learn new things at a regular pace. If you’ve mastered the basics of a subject so
that you can read denser books about it, reading books on the topic is mostly a
matter of putting in time, not developing ingenious learning strategies.
Of course, there’s a spectrum of habits, from zero-effort, spontaneous
engagement to the high-effort, rapid skill acquisition of ultralearning. Most
habits are somewhere in between, necessitating a bit of effort but perhaps not the
full-scale intensity of an ultralearning project. You may have learned enough
Excel to create your own spreadsheet macros, but you don’t always find
opportunities or time to use it, so you need to push yourself a little to practice.
You may have learned public speaking well, but it still takes some guts to go
onstage. The decision of whether the right step forward is to set up long-term
habits or to create a concentrated ultralearning project is often not crystal clear
and may depend more on your personality and life constraints than a hard-andfast rule.
Habits tend to work best when the act of learning is mostly a process of
accumulation, adding new skills and knowledge. Ultralearning and more
deliberate efforts are better suited to when improvement in a field requires
unlearning ineffective behaviors or skills. Increasing your vocabulary in a
foreign language is often a slow process of accumulation; you are learning words
you didn’t know before. Improving your pronunciation, on the other hand, is an
act of unlearning. You’re training yourself to use different muscular movements
that aren’t natural to you. Ultralearning also tends to be better for areas in which
learning has greater frustration barriers and psychological obstacles that make
any form of practice too great an effort to be an easily established habit.
Throughout this book, we’ve explored the trade-off that occurs between doing
what’s effective for learning and what’s easy and enjoyable. Sometimes what’s
the most fun isn’t very effective, and what’s effective isn’t easy. This trade-off
may push you to opt for easier, more enjoyable forms of learning that sacrifice
some effectiveness. However, in my own experience, I’ve noticed that
enjoyment tends to come from being good at things. Once you feel competent in
a skill, it starts to get a lot more fun. Therefore, although a tension between the
two can exist in the short term, I think pursuing aggressive ultralearning projects
is often the surer way to enjoy learning more, as you’re more likely to reach a
level where learning automatically becomes fun.
Alternative Strategy 2: Formal, Structured Education
Alternative Strategy 2: Formal, Structured Education
In the beginning of this book, I explained that ultralearning is self-directed,
although not necessarily solitary. Being self-directed is about who is making
decisions, not about whether other people are involved. Therefore, there is no
contradiction in pursuing ultralearning within a school or university. That might
be the best way to learn the skills you want to acquire. Just treat it like any other
resource.
That distinction notwithstanding, I think it’s worth talking about other reasons
you might want to pursue formal education rather than ultralearning. The most
obvious is to acquire credentials. If those are necessary or recommended for
your chosen line of work, you might need to be satisfied with making sacrifices
for your learning in order to acquire them. The message of this book isn’t that
you should drop out of school to learn on your own but that you should take
control over your own learning, wherever that may be. Another reason to pursue
formal education is that it creates a learning environment that may be beneficial.
Although many aspects of school are woefully indirect and ineffective, others
fare much better. Design and art schools often function like apprenticeships.
Some programs allow for team projects that are difficult to start on your own.
Finally, the postgraduate levels of academia create communities where
immersion is possible, so that you acquire not only the ideas that are written
down in books and papers but those that are communicated indirectly between
experts in their fields. Ultralearning isn’t a rejection of those opportunities, and I
would be disappointed if I were to be misread as arguing that they don’t exist or
are better replaced with a solitary learning effort. The correct mindset to
cultivate isn’t a rejection of anything slower or standardized but a recognition
that the possibilities for learning anything are considerably broader than they
might first appear.
Lifelong Learning
The goal of ultralearning is to expand the opportunities available to you, not
narrow them. It is to create new avenues for learning and to push yourself to
pursue them aggressively rather than timidly waiting by the sidelines. This is not
going to be a method suitable for everyone, but for those who feel inspired to use
it, I hope it provides a start.
Chapter XIV
An Unconventional Education
Give me a dozen healthy infants, well-formed, and my own specified world to bring
them up in and I’ll guarantee to take any one at random and train him to become any
type of specialist I might select—doctor, lawyer, artist, merchant-chief and, yes, even
beggar-man and thief.
—Psychologist John Watson
Judit Polgár is widely considered to be the best female chess player of all time.
At age seven, she won her first game against a chess master while blindfolded.
At twelve, she was ranked fifty-fifth of all chess players in the world by the
Fédération Internationale des Échecs (FIDE) (World Chess Federation). By
fifteen, she had become the youngest-ever grand master, beating the illustrious
Bobby Fischer’s previous record by one month. At her peak, Polgár was ranked
eighth in the world and competed in the World Chess Championship, the only
woman ever to have done so.
Chess is a game dominated by adult men. Thus a young girl competing was
bound to arouse both the curiosity and the prejudice of her competitors. Grand
master Edmar Mednis, facing off against the young Polgár, noted that he was
very careful to play his best against the young prodigy, noting that
“Grandmasters don’t like to lose to 10-year-old girls, because then we make the
front page of all the papers.”
1 Some of her competitors celebrated the obvious
genius of Polgár’s play. Grand master Nigel Short said Polgár might be one of
the “three or four great chess prodigies in history.”
2 Mikhail Tal, a former world
champion, suggested when Polgár was still twelve that she might eventually be a
contender for world champion.
Garry Kasparov was less convinced. The former world champion has been
considered by many to be the best chess player of all time. He is most famous
for his matches against IBM’s Deep Blue chess computer, winning against the
machine in 1996 and losing in 1997, marking the transition to machine
dominance in a game that had historically been considered one of the highest
expressions of human creativity and intelligence. Kasparov was less than
enthusiastic about the young Polgár’s chances. “She has fantastic chess talent,
but she is, after all, a woman. It all comes down to the imperfections of the
feminine psyche. No woman can sustain a prolonged battle.”
3
That casual prejudice erupted into a full-blown controversy during their first
match. Polgár, then only seventeen, sat across from the chess legend and former
world champion, playing in a tournament in Linares, Spain. Although chess is
often viewed as being coldly rational, as both players calculate moves with
precision to reach their final outcome, the psychological effect of sitting opposite
the dominant Russian can’t be understated. Given that incredible tension, it was
almost unbelievable when Kasparov, on move thirty-four, placed his knight and,
after briefly lifting his fingers from the piece, changed his mind and moved it to
a better square. Polgár was stunned. According to chess rules, once a player
stops touching a piece, the move is done; no changes are allowed. Half in
disbelief, she glanced at the referee, expecting him to indicate that Kasparov had
cheated. Yet the referee didn’t challenge the grand master. Reeling from the
move, Polgár lost the game.
Asked why she hadn’t challenged the illegal move herself, Polgár explained,
“I was playing the World Champion and didn’t want to cause unpleasantness
during my first invitation to such an important event. I was also afraid that if my
complaint was overruled I would be penalized on the clock when we were in
time pressure.”
4 Still, after the game had concluded, she was incensed. She
confronted Kasparov later in the hotel bar, demanding “How could you do this to
me?”
5 “She publicly accused me of cheating,” Kasparov said when defending
himself against the accusation. “I think a girl of her age should be taught some
good manners.”
6
It would be years before the two spoke again, but whereas
Kasparov was already well established in the chess world, Polgár was just
getting started.
Polgár is singular, not only for her prowess in the male-dominated game but
Polgár is singular, not only for her prowess in the male-dominated game but
also for how she learned to play. Unlike other famous players, such as Bobby
Fischer, who spontaneously developed an obsession for the game, Polgár’s chess
genius was no accident. Instead, it started with one man’s mission to raise genius
children.
The Making of a Genius
Years before Judit faced down the legendary grand master, before her meteoric
rise in chess or even her first game, her father, László Polgár, had made a
decision: he was going to raise a genius. While studying intelligence in college,
he had begun contemplating his project before having children or even a wife.
“A genius is not born but educated and trained,” he maintained.
7 Studying the
biographies of hundreds of great intellectuals, he was convinced that genius
could be manufactured. “[W]hen I looked at the stories of geniuses,” he later
remarked, “I found the same thing. . . . They all started at a young age and
studied intensively.”
8
But first, he had to find a partner for his pedagogical experiment. He found it
in Klára, a Ukrainian foreign-language teacher. Far from your normal love
letters, he first corresponded to her by explaining his idea to raise genius
children. After agreeing to his proposal, the two met and were married in the
Soviet Union before moving back to his native Hungary. Together the couple
had three children, Zsuzsa, Zsófia, and Judit. Although Judit ended up becoming
the most intensely competitive and famous, all three became world-class chess
players, with Zsuzsa also becoming a grand master and Zsófia reaching the
status of international master.
Living modestly in a cramped apartment, László and his wife decided to
devote themselves full-time to their project of raising genius children. Their
strategy was to begin the girls’ educations early, at age three, and move on to
specialization in one domain no later than six. They would start by introducing
the girls to that subject slowly, in short bursts, and turn it into play so that they
would actively want to practice rather than feel coerced into doing so. That
strategy, however, didn’t specify a topic. László and Klára considered many
different possible study topics for their daughters, from foreign languages to
mathematics. Eventually they settled on chess, because it was objective and
progress in it was easy to measure. No doubt the preeminence of chess
intellectually in the socialist countries in those years added weight to their
intellectually in the socialist countries in those years added weight to their
decision to focus on the game.
Despite the emphasis on chess, László did not believe that such specialization
needed to come at the sacrifice of the broader education of his girls. All three
learned foreign languages (Zsuzsa, the eldest, learned eight), as well as
mathematics, table tennis, swimming, and other subjects. The decision to focus
on chess with all three girls was made for a practical reason: given the intense
devotion both parents would need to have, in both resources and time, spreading
their effort over three different disciplines would have been more than their
budget or schedule could bear.
Zsuzsa was first to start. She began playing at age four. Six months later, she
was going with her father to the smoke-filled chess clubs of Budapest, playing
against elderly men—and winning. By the time it was Judit’s turn, she was
already motivated to begin her training. Zsuzsa and Zsófia played chess in a
small room László had devoted to the game, and she didn’t want to be left out.
Soon the three girls were a team, traveling around to compete against much
older, usually male players. Their shared mission created camaraderie rather than
jealousy in playing a game that very often pushed back against their unusual
status. The Hungarian Chess Federation’s policy was for women to compete in
women-only chess events. However, László was strongly against the idea.
“Women are able to achieve results similar, in fields of intellectual activities, to
that of men,” he felt. “Chess is a form of intellectual activity, so this applies to
chess. Accordingly, we reject any kind of discrimination in this respect.”
9
Discrimination had already prevented Zsuzsa from obtaining the grand master
title at fifteen. As the youngest, by the time Judit approached some of these
barriers, her elder sisters had already broken them down a bit, allowing her to
forgo competing in the women-only tournaments.
Although their education was the same and all three reached impressive
heights in chess, the girls’ prowess was not identical. Zsófia was the weakest of
the three; although reaching the impressive grade of international master, she
later decided to retire from chess to focus on art and her family. Zsuzsa had
specialized a little less in chess from an early age, learning eight languages,
which her father admits might have distracted her from reaching her maximum
chess potential. Judit started more slowly, according to Zsuzsa, but she had the
strongest work ethic, being “obsessed” with chess to a degree unusual even for
her family.
Rematch with Kasparov
Rematch with Kasparov
Eight years after her controversial defeat by Kasparov, Judit had another
opportunity to face down the legendary grand master. In the 2002 Russia Versus
the Rest of the World match in Moscow, Judit faced off against Kasparov in a
game of rapid chess, with only twenty-five minutes allocated to each player.
Judit played the Ruy Lopez, or Spanish game, named after the sixteenth-century
Spanish bishop and chess strategist. This uses one of the most common openings
in chess, moving the knight and bishop out to good squares on the second and
third moves. Kasparov countered with the Berlin Defense, moving a second
knight to the board while ignoring the potential danger created by his opponent’s
bishop; this was known to be a solid counter, often leading to draws. Kasparov
wasn’t taking any chances. After a flurry of exchanged pieces, both positions
were quite close. Judit, as white, had castled her king into safety. Kasparov, as
black, in losing the opportunity for the same safety had retained both the lightsquare and dark-square bishop pair, a powerful combination that can often be
decisive in winning a game. Judit calmly pushed forward, cornering one of
Kasparov’s bishops and neutralizing his advantage. Slowly but surely, her moves
continued to improve her position while Kasparov’s looked increasingly
questionable. Eventually, the small positional advantages Judit had accumulated
over the midgame were threatening to win. Two pawns down and facing
looming threats of checkmate, Kasparov resigned.
Following his defeat, Kasparov revised his earlier assessment of Judit’s ability
and indeed the idea of women competing against men at the highest levels of the
game. “The Polgárs showed that there are no inherent limitations to their
aptitude—an idea that many male players refused to accept until they had
unceremoniously been crushed by a twelve-year-old with a ponytail.”
10
The Aftermath of an Experiment
László Polgár’s conviction that he could turn any healthy child into a genius is
the kind of assertion that would make him seem like a crackpot if he hadn’t
actually been successful. Nonetheless, a careful reader will note that, as far as
experiments go, László’s has plenty of holes that keep it from becoming a model
of scientific purity. There was no control group, for starters. All three of the
Polgár sisters received the same education. There was no fourth sister who went
to school the normal way and missed László’s special training. There was no
to school the normal way and missed László’s special training. There was no
randomization. László didn’t adopt a random child to raise in his unusual system
but taught his own. This also means that the influence of genetics can’t be
ignored. The success of all three children may be owing to hereditary, rather
than acquired, talents. There was also no blinding. All the Polgárs knew they
were part of something special, a unique mission that set them apart from other
families. Therefore, in the ongoing debate between nature and nurture, the
success of the Polgár sisters might be suggestive of the role that unorthodox
education can play, but it is far from definitive.
Despite its failings as a purely scientific experiment, the Polgárs’ work is
certainly a window into what might be possible. All three girls achieved
enormous results in chess. Although we cannot know for certain, it seems likely
that they could have succeeded in any number of other domains as well.
Similarly, although László’s methods were strange, it does not seem as though
the girls suffered, either in their broader education or in their emotional wellbeing. They were self-confident and happy and grew up to be successful,
emotionally stable adults with loving families of their own. When asked whether
his strange pedagogical methods had robbed his girls of a normal childhood,
László argued the opposite, that it is a normal, mediocre education that often
leads to unhappiness. Interviewer Endre Farkas, who worked with Polgár on his
book Raise a Genius!, asked, “Have they been educated too narrowly, deprived
of a carefree childhood? I was able to observe the Polgárs. . . . One can clearly
see that they are happy.”
11
Educating Ultralearners?
Prior to doing the research for this book, all of the ultralearners I had met were
ambitious self-starters. I was convinced that ultralearning was something that
held great potential for the individual. However, owing to the intensity and
commitment required by the learners themselves, I was skeptical that
ultralearning would have any direct implications for the educational system at
large. Children already struggle against onerous studying conditions, and it
seemed to me that increasing the intensity of study would only increase their
stress and anxiety.
Psychologists recognize a large difference between goals that people pursue
intrinsically, based on their own interests, decisions, and targets, and goals that
they pursue extrinsically, pushed by overbearing parents, punishing curricula, or
they pursue extrinsically, pushed by overbearing parents, punishing curricula, or
demanding employers. The latter type, because the motivation for conforming to
them comes primarily from outside social pressures, are the cause of much
misery. Tales of depression, anxiety, and even suicide are distressingly common
in environments where the pressure to perform on standardized tests is ratcheted
up to an uncomfortable degree. Ultralearning, because it is a self-driven quest,
not an externally imposed obligation, doesn’t need to be this way. However,
because of its very nature, it was unclear to me whether it was something that
could be taught.
The Polgár sisters are an unusual case, then, because although they were
trained from a very early age and worked incredibly hard, they don’t seem to
have been harmed psychologically by the pressure. In contrast to the
stereotypical “tiger” parent, their parents encouraged their unusual specialization
through play and positive feedback, not authority and punishment. The Polgár
sisters all continued to play chess well into adulthood at the competitive level.
The obsession into chess, therefore, was clearly something fostered, rather than
imposed. At the same time, their participation in the experiment wasn’t exactly
voluntary. László had dreamed up his goal of raising geniuses before knowing
whether his children would consent to the program, so it was not a case of each
daughter discovering for herself that she wanted to devote herself to an intense
regimen of chess practice. That feature of the Polgár experiment interested me
the most, because it seemed as if László and Klára had found a loophole in the
normal expectation that pushing others to study intensely necessarily leads to
misery.
How to Raise an Ultralearner
László Polgár wrote a book entitled Raise a Genius!, documenting his
unorthodox approach to education.* In the book, he outlined his strategy for
turning any normal, healthy child into a genius, provided the parents are willing
to go to the extremes he and his wife dedicated to the task.
The first step is to start early. The child’s education should begin no later than
three, and specialization should begin no later than six. Although the exact
extent to which learning becomes harder as we age is unclear, there is evidence
from fields such as music and languages that children’s brains are more plastic
and flexible when younger. László took this idea to the extreme, encouraging
training far earlier than is typical for early-childhood education.
training far earlier than is typical for early-childhood education.
The second step is to specialize. Although the Polgár sisters did learn
languages, mathematics, sports, and other subjects, their focus was always on
chess. László noted that “starting from 4–5 they played chess 5 or 6 hours a
day.”
12 That specialization seems to have had two roles in his strategy for raising
geniuses. The first is that it took advantage of any hypothetical flexibility that
younger children possess to learn new subjects easily. The second is that by
specializing in one subject, the children could reach proficiency at a much
younger age. Winning against older and more experienced opponents in chess
built their confidence and competitive spirit, so that they actively wanted to
practice more to improve themselves. Had the girls spread their intellectual
appetites too broadly, they might not have developed the self-confidence that
leads to intense practice.
The third step was to make practice into play. Chess, being a game, is
naturally suited to play. However, László was insistent on introducing all
subjects to the girls as forms of play. When the girls would become distracted or
get up and wander around while playing a game, they weren’t punished but
encouraged to let their minds wander as they sought a solution. Keeping the
game fun and light, especially when the children were young, was a key
stepping-stone to developing the drive and self-confidence that would support
more serious efforts later. However, it is important to keep in mind as László
insisted that “play is not the opposite of work” and “a child does not need play
separate from work, but meaningful action,” adding “learning presents them with
more enjoyment than a sterile game.”
13 Play and work combined in the Polgárs’
approach to learning, with no rigid boundary between them.
Fourth, László was careful to create positive reinforcement to make chess a
pleasant, rather than frustrating, experience. “Failure, suffering, and fearfulness
decrease achievement. Following a number of successive failures, even a
damaging inhibitory complex can be created,” he explained.
14 Starting with the
behaviorists, psychologists have known in detail that having a positive
experience, such as winning a game, can create a desire to repeat the behaviors
that led to the experience. Negative experiences, from losing, becoming
confused, or experiencing frustration against a stronger opponent to facing an
opponent who is too easy and robs the player of the satisfaction of pulling off an
impressive victory, reduce enthusiasm. László carefully established the cycles of
positive feedback early on. In the beginning, when he was still a stronger player
than the girls, he would adjust his play so that they would be challenged but still
win enough of the time to find the game enjoyable. “We should make sure not to
always win against the child; we should let them win sometimes so that they feel
they are also capable of thinking,” he wrote, adding “At the start it is most
important to awake interest. . . . We should get the child to love what they do—
to such a degree that they do it almost obsessively.”
15
Finally, László was entirely against coercing learning. Self-discipline,
motivation, and commitment, he felt, must come from the girls themselves. He
explained, “One thing is certain: one can never achieve serious pedagogical
results, especially at a high level, through coercion.”
16 He also felt that “one of
the most important educational tasks is to teach self-education.”
17 This final step
of his process was particularly important for his daughters, as they quickly
outpaced their father’s ability. Had they not been encouraged to develop their
own abilities to teach themselves and adjust their learning, they might have
become decent chess players but certainly not grand masters.
In addition to these basic principles, László and Klára devoted themselves
intensely to providing every opportunity for the advancement of their daughters,
cultivating a database of more than two hundred thousand matches, buying every
chess textbook they could find, and recruiting chess tutors for their daughters.
The girls lacked no opportunity to study and improve at the game. The Polgár
house, with diagrams of chess positions hanging on the walls, became a temple
devoted to the practice of the ancient game. For László and Klára, raising their
children was more than a full-time job as they fostered the girls’ talents by both
assembling resources and schooling them at home.
Ultralearning Principles in Action
In addition to Polgár’s principles of raising genius children, I found it interesting
that all of the principles of ultralearning I’ve discussed this far were present in
their approach to learning.
1. Metalearning
Polgár devoted himself full-time to understanding how people learn chess and
under what conditions his daughters would thrive. He created a huge library of
chess positions, strategies, and lists of games, which, in the days before
widespread internet, was no small feat. He also articulated a plan for coaching
widespread internet, was no small feat. He also articulated a plan for coaching
the girls in the game when they were still very young, starting by teaching them
first to name the squares on the board and later to know how the pieces moved.
That slow progression allowed the girls to pick up the game even before their
other cognitive abilities had developed.
2. Focus
László considered “the ability to handle monotony, the capability to sustain
interest and persistent attention” as key traits he aimed to instill in his daughters.
The girls twice took part in twenty-four-hour chess marathons when they were
fifteen, nine, and eight years old, respectively, needing to complete one hundred
matches in the time period. Chess is a game not merely of flickers of brilliance
but of endurance and stamina. Training focus was a large part of László’s system
for his daughters, as he encouraged them to focus their minds on the problem
and not get distracted.
3. Directness
László took his daughters to games with men when they were as young as four,
showing them how the game was to be played against opponents who would
really offer a challenge. The girls played many, many games of chess, which
formed the backbone of their abilities. That allowed them to learn not only how
to play the game well but also to deal with variables such as time pressure and
the psychological insecurities of playing against older, more intimidating
opponents. By using chess timers even for casual games, the girls practiced in an
environment that more closely matched the one they would face in tournaments.
4. Drill
László varied the approaches to studying the game, starting his daughters off
with memorizing first the names of the squares, then the movements of key
pieces. Chess puzzles, hanging from the walls of the house, became the girls’
homework, as they had to solve the different tactical positions and come up with
creative solutions. Blitz and blindfolded games allowed the girls to get better at
thinking more quickly and mentally simulating the game.
5. Retrieval
For retrieval, László explained, “We should not tell them everything; we should
try to get the child to say something!” Using what he described as the “Socratic
method” for chess, posing questions his girls must answer instead of telling them
to remember a presolved solution, he was using the right method to encourage
the expansion of their memory and understanding. Blindfolded games, once
again, formed a powerful component of the girls’ strategy. By practicing without
looking at the board, it forced them to cultivate the ability to follow positions in
their head, which was useful not only for retaining key chess patterns long term
but also for honing the ability to simulate moves on the board that an opponent
might play.
6. Feedback
László encouraged considerable play with real opponents but was careful to
select “suitable partners, who have a generally similar playing ability.”
Interestingly, the feedback here was carefully controlled, not only to provide the
girls with enough challenge (the Polgárs’ insistence on playing in men’s
tournaments in order to face such a challenge was an example of this) but also to
avoid too great a challenge when their abilities were still nascent. Cultivating
positive feedback was important early on, and László was ever ready to adjust
the flow of the game to make sure it was at a level that would stimulate further
play.
7. Retention
László focused on having the girls recall chess patterns from memory and
increased the speed of games to make elements of their play more automatic and
less susceptible to forgetting. Memorizing chess patterns is a large part of
playing the game successfully, and this was aided both by spaced practice and
through specialized drills such as blitz and blindfolded games.
8. Intuition
Mirroring the Feynman Technique, László encouraged his girls to write articles
Mirroring the Feynman Technique, László encouraged his girls to write articles
about chess, explaining, “If one writes an article, one considers a matter more
deeply than without a goal, thinking alone or speaking with someone about it.”
The girls were also encouraged to come up with creative solutions to problems.
Play, not merely in the sense of chess being a game but also in the sense of an
unconstructed, goalless activity, was part of the teaching strategy. Coming up
with interesting solutions and challenging the girls to think of tricks and new
insights allowed them to explore outside what could be offered by memorizing
past results.
9. Experimentation
As the Polgár sisters eclipsed their father in chess ability, their impetus to
continue mastering the game increasingly had to come from within themselves.
Each of the girls had to cultivate her own unique style and approach. Judit chose
to focus on tricks and tactics, writing that “opening preparation was not at all
important at that time. This may be a reason why, even today, my strongest area
remains the middlegame.”
18 The girls’ varying choices show that chess, like any
creative skill, involves not merely a mastery of patterns but also choices about
what skills and styles to cultivate within a vast range of possibilities.
Finally, the Polgárs embodied the idea of ultralearning at its broadest, with
László arguing, “In my opinion, we should disseminate the idea of intensive
learning in every field.” The success of the Polgárs follows the same pattern as
that of most of the ultralearners I have met: aggressive, enthusiastic selfeducation following the key principles of learning.
Fostering Ultralearning in the Home, School, and
Workplace
How can you foster ultralearning as a parent or educator or in an organization? Is
it possible to help others self-confidently tackle difficult learning projects of
their own design? Can you teach students not just the material they need to study
but how to learn on their own, so they will be self-sufficient outside the
classroom? Can you lead the individuals in your organization to learn more
aggressively, filling gaps in their competency and achieving their full potential?
These are all intriguing questions to which we don’t yet have definitive answers.
These are all intriguing questions to which we don’t yet have definitive answers.
In reading the scientific literature on learning and following up with the
ultralearners’ stories, I was struck not only by how much is known about
learning already but by how many open questions exist for which researchers
and autodidacts are still hazarding hypotheses. The complications expand
exponentially once you introduce the social environment as well. Now it’s no
longer a question simply of individual cognition but of the emotions, culture, and
relationships that start to influence learning in complex and unexpected ways.
From this perspective, therefore, I’d like to cautiously suggest some starting
points for fostering an environment that will support ultralearning, at home, in
the school, or in the workplace. These suggestions aren’t rules, but they can be
seen as starting points for enabling others to capture the ultralearning spirit.
Suggestion 1: Create an Inspiring Goal
Better yet, allow people to design their own learning goals that inspire them.
Inspiration is an essential starting point in the process of ultralearning. There
must be something very compelling for a person to summon up the energy and
self-discipline needed to learn. Sometimes that is the promise of a new skill
bringing career opportunities. Coding boot camps, which have sprung up in the
wake of high-paying programming jobs, push students through at a brutal pace,
sometimes approaching eighty hours a week. The goal, however, is compelling
enough to justify this investment: complete a rigorous program over the course
of a few weeks, and you can rise up the ladder of high-salaried tech jobs in
Silicon Valley and elsewhere. The process is intense, but the motive is
compelling.
In other cases, the motivation for ultralearning comes from an intrinsic interest
that becomes amplified. My own MIT Challenge started with feeling I had
missed out by not studying computer science in school. Normally, that wouldn’t
have led to any large, structured effort to learn a lot of computer science. It was
only when the idea of doing an entire degree in a shortened period of time, along
with the research that made me think it might be possible, that my initial interest
become a passionate commitment. Roger Craig, with his Jeopardy! exploits, was
always interested in trivia competitions. It was only when he recognized that
there might be a chance to appear on the famous television show that his interest
became an obsession. Eric Barone took his love of a childhood video game and
expanded it into an effort to create a better version. Seeking out people’s natural
interests for ultralearning means encouraging the sparks that already exist, rather
than merely imposing on them the topics you feel would be most beneficial.
Once people see the structure of an ultralearning project, they can start thinking
for themselves what would be most interesting, exciting, and useful for them to
work on. Tristan de Montebello started with the idea of ultralearning and only
later chose to craft a public speaking project around it.
Suggestion 2: Be Careful with Competition
The Polgárs’ example clearly indicates that early self-confidence can create an
enthusiasm that leads to continued investment. You don’t need to feel as though
you’re good at something to invest energy into learning. After all, becoming
good at something is what learning is. However, you need to feel that you could
be good at it. People tend to make their perceptions of inadequacy into
immutable destinies: “I’m no good at math,” “I can’t draw anything but stick
figures,” “I don’t have the language gene.” Although there likely do exist real
differences in innate ability, so that these pronouncements aren’t completely
false, they tend to ignore an important factor: motivation. When you see yourself
as lacking the potential to be good at something or believe that you’ll always be
behind everyone else no matter how hard you work, it robs you of the motivation
to work hard. Thus, although there are differences in ability among all of us,
they can often be exacerbated by the affective dimension they create in how we
feel about learning. Feel as though you’re lousy at doing something, and you’re
robbed of the motivation to change.
The reference group you compare yourself to can have a powerful influence. I
find it interesting that many, but not all, ultralearners aimed at projects that were
so unusual that they made it hard to compare them to a normal reference group.
De Montebello’s public speaking competition certainly pitted himself against
excellent public speakers. That might have created a feeling of inferiority, except
that de Montebello could always explain to himself any perceived deficits as
coming from trying such an ambitious project with so little prior experience.
Had it been the case that instead of an individual ultralearning project, he had
been pitted against a dozen other competitors with exactly the same prior
experience, he might instead have rationalized any perceived inadequacy as his
simply not being good enough. This suggests that the competitiveness of the
project cuts both ways: When you have a natural talent and thus perform much
better than the easily identifiable reference group, you’ll have more motivation
to practice and learn with intensity. However, if you come up short, it may rob
to practice and learn with intensity. However, if you come up short, it may rob
you of the motivation to practice. The Polgárs used competition to their
advantage. Because the girls’ training started unusually young, they were always
seen as precocious and the competitive environment enhanced their motivation.
Had they started late or been put into a school where they weren’t guaranteed to
be star performers, their motivation might have been sapped.
To me, such motivational effects, coming from implicit comparison to a
reference group, suggest adopting a twofold approach. If a person in whom you
want to encourage an ultralearning spirit has a natural aptitude, competition is
probably good. Seeing him- or herself do well in direct comparison with others
may encourage a commitment to further improvement. For a person who either
is of moderate ability or is behind other people, such as learning a skill in a
domain in which he or she has no experience, or who is starting to learn a new
skill later in life, you should make an effort to make the project unique. This will
encourage the person to frame his or her progress by comparing to his or her past
self, not due to competition with others. Sometimes a project can start out being
unique, thus sheltered from the harsh light of unfavorable comparison, and move
to a more competitive environment once confidence has been established. For
example, you might start learning programming by creating a game that is hard
to compare to others but enter coding competitions as you begin to feel more
competent.
Suggestion 3: Make Learning a Priority
Outside school, learning is usually seen as a by-product of doing a job, not the
core goal. Though organizations often give lip service to ongoing training and
education, it’s usually in the form of workshops or seminars that one sits through
passively before getting back to the real job at hand. Ultralearning, by
encouraging direct, intensive practice, provides the opportunity for a kind of
fusion project—one that accomplishes real objectives but is also designed to
teach something new.
The normal protocol for assigning a project is to find the best person for the
job and give them the task. A learning-driven approach would suggest instead
that people who are not yet capable of doing the task might be assigned to the
project. An ultralearning-driven work environment might consist of employees’
spending a majority of their time on projects that are within or near their
competency levels but devoting a specific fraction of their time to working on
projects that are a leap above their current abilities. Although this is purely
projects that are a leap above their current abilities. Although this is purely
hypothetical, I imagine two benefits of this approach. First, it will create a
culture of learning within an organization where people are always willing to try
to solve problems they don’t yet know how to solve, instead of expecting
someone else to know the answer already. Second, it will help reveal talent by
giving people challenges they can rise to. If mentorship opportunities and
difficult projects are assigned only on a whim by managers, they will likely miss
a lot of people who may have the ability to succeed in difficult positions but are
never given the opportunity to do so.
At the highest levels, an ultralearning-driven culture also allows learning to go
into areas where perhaps nobody else has a particular skill. Although going
between established levels of skill is important, it is when one learns to do
something that nobody else can do that learning becomes truly valuable.
Conclusion
In many ways, writing this book has been an ultralearning project. Although a
writer researching for a book is hardly unique, not all ultralearning projects need
to be one of a kind to matter to the person doing them. Sitting in my den at home
are stacks of binders filled with thousands of pages of printed journal articles.
My bookshelf now has dozens of obscure, out-of-print monographs on thin slices
of the question of how people learn. Recordings of calls with various researchers
helped me realize how much nuance there is to even simple questions such as “Is
feedback helpful?” and “Why do people forget?” I’ve poured over numerous
biographies of famous intellectuals, entrepreneurs, and scientists to try to arrive
at an understanding of how they approached learning. In many ways, the process
of writing this book was a reflection of its subject—an ultralearning project to
write a book about ultralearning. Although I had a strong interest in the subject
of learning and had browsed textbooks, articles, and biographies before I began
research into this book, it was only after I started this structured project that I
really began to dig deep.
Beyond research, this book was a challenge for me as a writer. My writing
experience comes from blogging, not authoring books. Striking the right tone in
a book is hard, and it’s quite different from the casual daily missives in a blog. I
knew from the start that I wanted to share the stories of others and their exploits,
not just recount my own experiences. That was initially quite challenging. Most
biographies and published stories don’t focus on learning methods. Even when
biographies and published stories don’t focus on learning methods. Even when
learning is the central theme of the story, most biographers are satisfied to be in
awe of talent, rather than dig into the specific details of how a person did a
particular thing. My research efforts frequently involved scouring a fivehundred-page biography for the several paragraphs in which concrete details
about learning methods were mentioned in passing. Although this created
challenges, it also forced me to develop new skills as a writer. I had to improve
my research and writing skills in ways that more than a decade of penning blog
articles never had. Even the style of the book created a skill-challenging project
for myself. I’ll leave it to you, the reader, to judge whether I was successful.
The metaproject of ultralearning to write a book about ultralearning also
illustrates some important ideas. For one, although I’ve made enormous
improvements in my writing ability and knowledge of cognitive science and
stories of famous learning exploits, there is still far more to learn. Digging into
the science, for instance, one can quickly develop a sense of vertigo standing
atop the mountain of papers, theories, ideas, and experiments, all loosely
connected to the topic of learning. Similarly, for every biography I read, there
were hundreds I could not. For every ultralearning story I encountered, there
were likely dozens more my searches didn’t reveal. It is a profound error to
claim that learning is about replacing ignorance with understanding. Knowledge
expands, but so does ignorance, as with a greater understanding of a subject also
comes a greater appreciation for all the questions that remain unanswered.
In the face of this, one must simultaneously have confidence and deep
humility. Without the belief that progress in one’s own knowledge and skill is
possible, one cannot undertake the project required to generate it. This kind of
confidence may be mistaken for arrogance by outsiders, as it can seem that an
effort to learn something quickly and intensely is somehow an assertion that the
subject is trivial or that, having learned something, one has learned everything.
Instead, this confidence must be paired with deep humility. In every project I’ve
undertaken, including this one, my thoughts upon concluding it were not to think
I had finished but to suddenly become aware of how much further I could have
gone. Before I started my MIT Challenge, I imagined that covering an
undergraduate degree’s worth of computer science concepts would be plenty.
After I had finished, I could see how each topic I had learned could be multiplied
into a doctorate’s worth of research or a lifetime spent coding to fully understand
it. My experience in learning languages to a level where I could hold
conversations made me realize how many more words, expressions, nuances of
culture, and difficult communication situations were left to explore. Finishing a
culture, and difficult communication situations were left to explore. Finishing a
project, therefore, isn’t usually accompanied by a sense of finishing learning but
by the creation of a feeling of possibility as one’s eyes are opened to all the
things left to learn.
It’s this aspect of learning that I find most interesting. Many pursuits in life
have a kind of saturation point, after which the longing for more of a thing
eventually diminishes as you get more of it. A hungry person can eat only so
much food. A lonely person can have only so much companionship. Curiosity
doesn’t work this way. The more one learns, the greater the craving to learn
more. The better one gets, the more one recognizes how much better one could
become. If you finish reading this book and have been encouraged to try your
own project, this would be my greatest hope—not that you’d be successful at
your project but that your ending would be a beginning. That by opening a small
crack in all the possibly knowable things there are in the world, you might peer
through and find there is far, far more than you had ever imagined.
Acknowledgments
This book could not have happened without the help, advice, and work provided
by many different people. First, I would like to thank Calvin Newport. Had it not
been for his early encouragement, I might never have pursued writing a book
about this topic. I would also like to thank Benny Lewis, whose early inspiration
and endless advice over the years has had such a strong influence on my
thoughts on learning and writing. Laurie Abkemeier, my agent, was instrumental
in taking my rough ideas in a proposal and pushing me to develop something
worthy of print. I thank Stephanie Hitchcock for editing the book and providing
me with excellent feedback and suggestions. I’m also thankful to my friends and
family who read over early drafts of the proposal and manuscript, helping the
idea take shape. In particular, I would like to thank Zorica Tomovska, Vatsal
Jaiswal, Tristan de Montebello, James Clear, Josh Kaufmann, Kalid Azad, and
Barbara Oakley for their early feedback.
I would like to thank the wonderful people I met and interviewed while
preparing for the book. I am grateful to Roger Craig, Eric Barone, Vishal Maini,
Diana Jaunzeikare, Colby Durant, and Vatsal Jaiswal, who were kind enough to
take time to help me fill in the details of their incredible stories. I want to thank
many of the researchers who walked me through their findings and helped me
understand the science of learning better. In particular, I want to thank K. Anders
Ericsson for his patience as he helped me clarify many important points. In
addition, I thank Robert Pool, Jeffrey Karpicke, Angelo DeNisi, Avraham
Kluger, Jacqueline Thomas, and Michael Herzog for helping me understand the
nuances of the science discussed in this book. I want to thank all the people who
participated in my experiments with coaching ultralearning: Tristan de
Montebello, Jeff Russell, Diana Fehsenfeld, Kate Schutt, Lissa Sherron, Joshua
Sandeman, Keerthi Vemulapalli, Brittany Hsu, Shankar Satish, Ashima
Sandeman, Keerthi Vemulapalli, Brittany Hsu, Shankar Satish, Ashima
Panjwani, Ashfaq Alsam, Deepti Kannapan, and Ankita J.
Finally, I want to thank my parents, Douglas and Marian Young, both
teachers, who taught me that learning is its own reward.
Appendix
Further Notes on My Ultralearning Projects
The MIT Challenge
GOAL: Learn the material taught in MIT’s undergraduate curriculum for
computer science, using their freely provided materials and used
textbooks
METHOD: Aim to pass all of the final exams (score over 50 percent,
unless other information was provided) and complete the programming
projects
TIMEFRAME: October 2011 to September 2012
Notes and Discussion
It’s important to note that what I ended up completing was not a facsimile of an
MIT degree. Although I strove, whenever possible, to benchmark the overall
curriculum covered and the intensity of evaluation, there were necessary
departures from how an actual MIT student would have progressed through the
same material.
At the level of the entire curriculum there were changes. MIT’s
OpenCourseWare didn’t offer options for humanities that I could grade myself at
the time, so I swapped those for classes in economics. Lab-heavy classes for
which I didn’t have access to the equipment were substituted for pencil-andpaper theory classes I could do. MIT students were expected to carry out a thesis
project. I didn’t do that during my twelve-month study period, but for fun I did
project. I didn’t do that during my twelve-month study period, but for fun I did
create a computer program that would allow someone to play Scrabble against a
computer opponent shortly after my project had officially concluded. In
evaluating the programming projects, I simply counted them as a success if they
worked and performed the desired functions or were able to complete the
accompanied testing suites.
For final exams, my default benchmark was to achieve at least 50 percent. I
stuck to the official grading rubric whenever possible. When there were gaps
(such as how to deduct points for arithmetic or algebraic errors on multistep
problems), I used my judgment. The latter stage introduced some potential bias,
so I decided to go back several years after completing the challenge and
reevaluate all my exams using the strictest possible grading scheme (any mistake
on a multipart question would make the entire question worth zero points; any
incorrect result applied to further questions would make those questions also
worth zero points). The outcome was that six of the thirty-three classes I had
recorded as a “pass” would have been counted as a “fail” under this stricter
schema. I don’t believe this evaluation is the correct one, so I stand by my
original evaluation of having passed those exams, but it is worth pointing out to
show how much impact my subjective decisions had. A few classes had no final
exams, so in those cases evaluation defaulted to assignments or midterm exams.
Completing assignments was not a requirement to complete a class, however I
did end up doing many of them as part of the learning process.
For more information on the challenge, such as course lists, materials used
and scans of my exams, you can visit the challenge homepage:
www.scotthyoung.com/blog/mit-challenge/.
The Year Without English
GOAL: Learn Spanish, Portuguese, Mandarin Chinese, and Korean
METHOD: Avoid speaking English for the entire year, while traveling to
Spain, Brazil, China, and South Korea (roughly three months each). I
did this project along with Vatsal Jaiswal (who is also mentioned in
chapter six).
TIMEFRAME: September 2013 to August 2014
Notes and Discussion
Notes and Discussion
Quantifying the level of proficiency reached in each language is a tricky task.
There’s a dual risk, both of exaggeration—implying a perfect level of fluency
when that is probably a process requiring decades of immersion—and of
downplaying. One person I talked to after the trip asked if I could “give a taxi
driver directions” even though this task only requires a few hours of practice, not
months. So with those difficulties in mind, I’ll try to estimate the level that we
reached:
SPANISH: Here I believe both my friend and I reached a roughly B2 level
after three months, meeting Benny Lewis’s standard for fluency (although
certainly not everyone’s standard). At that level, we had little difficulty
socializing for hours on any topic in Spanish, although certainly our accent,
grammar and more formal speaking abilities were not at the level of a
native speaker.
PORTUGUESE: We were weaker in Portuguese than in Spanish, although
not substantially so. The two languages share a common base, so there was
much less to learn than there had been with Spanish. We could make
friends and socializing, but not quite as effortlessly.
MANDARIN CHINESE: This marked the first big divergence in our abilities.
I had really wanted to learn Chinese and had spent some time on flash cards
prior to our trip to familiarize myself. My friend was less interested and
struggled more. In the end, I wrote and passed the HSK 4 exam (the fourth
in a six-level series of exams measuring Chinese proficiency) and I would
say my Mandarin was decent, although more limited on advanced topics,
where the vocabulary is completely different from English. My friend
reached a lower-intermediate level, being able to speak comfortably and use
tones but with less vocabulary.
KOREAN: In this language we both reached a lower-intermediate level, able
to have conversations and get by in daily life, but on a more restricted range
of topics. Part of this was the difficulty of the Korean language, but a bigger
part was simply that as it was the fourth new language in a row, we were
getting burned out.
Although we aimed to do most of our learning after arriving in each country,
we did do some prior preparation for each. This was mostly listening to Pimsleur
audio tapes and doing some flash cards. In generally we spent around twenty-
audio tapes and doing some flash cards. In generally we spent around twentyfive to fifty hours per language, although I spent more on Chinese
(approximately one hundred hours) prior to arriving.
Those interested can see more about our project (including videos we put
together showing our progress in each country), what we used to learn as well as
unscripted interview to show roughly the level we reached in each language on
the project homepage: www.scotthyoung.com/blog/the-year-without-english/.
Portrait Drawing Challenge
GOAL: To improve my ability to draw faces realistically
METHOD: Rapid feedback, techniques from various books and courses
TIMEFRAME: July 2016
Notes and Discussion
This was a shorter project, taking one month and totaling one hundred hours of
practice. In addition to the strategy of drawing quick sketches and comparing
them by overlaying them on semi-transparent reference photos, I also greatly
benefitted from the book Drawing on the Right Side of the Brain, and from
Vitruvian Studio’s Portrait Drawing class.
I’ve uploaded every drawing, sketch, and self-portrait I did, along with a more
detailed discussion of what I used to learn on the project homepage:
www.scotthyoung.com/blog/myprojects/portrait-challenge/.
Further Challenges
At the time of writing this book, the above three challenges are my main public
ultralearning projects. However, I’m always learning new things, so as I do more
public challenges, I’ll post them here: www.scotthyoung.com/blog/my-projects/.
Notes
Chapter I: Can You Get an MIT Education Without Going to MIT?
1. The Goethe-Institut, which administers: “Further Information,” Goethe-Institut,
https://www.goethe.de/en/spr/kup/prf/prf/gc2/inf.html.
2. “My first thought wasn’t ‘Wow’”: Thanh Huynh, Roger Craig—Knowledge Tracking,
filmed August 2011, YouTube video, 14:20, posted November 2011,
https://www.youtube.com/watch?v=jmld3pcKYYA&t=1s.
3. “Everybody that wants to succeed at a game”: “How One Man Played ‘Moneyball’ with
‘Jeopardy!,’” National Public Radio, https://www.npr.org/2011/11/20/142569472/howone-man-played-moneyball-with-jeopardy.
4. Spaced-repetition software is: Gary Wolf, “Want to Remember Everything You’ll Ever
Learn? Surrender to This Algorithm,” Wired, April 20, 2008,
https://www.wired.com/2008/04/ff-wozniak/?currentPage=all.
5. “You can simulate the game”: Huynh, Roger Craig—Knowledge Tracking.
6. “incredibly endearing and beautiful”: Patrick Hancock, “Review: Stardew Valley,”
Destructoid, March 7, 2016, https://www.destructoid.com/review-stardew-valley345495.phtml.
7. “This is the type of person”: “College Too Expensive? This Guy Just Finished a Four
Year Computer Science Program in ONE Year Using Free MIT Material” (video), Reddit,
https://www.reddit.com/r/videos/comments/10tk9j/college_too_expensive_this_guy_just_finished_a/.
8. Done without the benefit: Steve Pavlina, “Graduating College in 3 Semesters,” December
4, 2005, https://www.stevepavlina.com/blog/2005/12/graduating-college-in-3-semesters/.
9. Diana Jaunzeikare embarked on: Diana Jaunzeikare, “Personal PhD.”
https://diana.is/personal-phd.
10. “70–80+ hours each week”: Tamu, “Independent Chinese Study: Review,” Chineseforums.com, https://www.chinese-forums.com/forums/topic/43939-independent-chinesestudy-review/.
11. Trent Fowler, starting in early 2016: Trent Fowler, The STEMpunk Project (Selfpublished, 2017).
Chapter II: Why Ultralearning Matters
1. “Average is over”: Tyler Cowen, Average Is Over: Powering America Beyond the Age of
the Great Stagnation (New York: Penguin, 2013).
2. The MIT economist David Autor: David H. Autor, Lawrence F. Katz, and Melissa S.
Kearney, “The Polarization of the U.S. Labor Market,” American Economic Review 96,
no. 2 (May 2006): 189–94.
3. Tuition has increased far faster: Danielle Douglas-Gabriel, “College Costs Rising Faster
than Financial Aid, Report Says,” Washington Post, October 26,
2016,https://www.washingtonpost.com/news/grade-point/wp/2016/10/26/college-costsrising-faster-than-financial-aid-report-says/?utm_term=.72c95b4c86cb.
4. “a leading English-language novelist”: Gareth Cook, “The Singular Mind of Terry Tao,”
New York Times, July 24, 2015, https://www.nytimes.com/2015/07/26/magazine/thesingular-mind-of-terry-tao.html.
Chapter IV: Principle 1—Metalearning: First Draw a Map
1. “Kuti paoka djalou”: Linguistic Society of America, “‘Monolingual Fieldwork
Demonstration’—Daniel Everett,” filmed July 2013, YouTube video, 1:16:27, posted
September 2013, https://www.youtube.com/watch?v=sYpWp7g7XWU.
2. What makes this feat particularly impressive: To avoid ruining the demonstration by using
English, a language the other speaker might have been familiar with, Everett chose to
phrase all his initial queries in the Pirahã language, spoken only by a remote people in the
Amazon jungle of Brazil.
3. Over the last thirty years: The unusualness of this language has led to somewhat of a
controversy in linguistics, with Dan Everett’s claims about Pirahã’s grammar at center
stage in an attack on linguistic orthodoxy.
4. To see why metalearning is so: Jacqueline Thomas, “The Role Played by Metalinguistic
Awareness in Second and Third Language Learning,” Journal of Multilingual and
Multicultural Development 9, no. 3 (1988): 235–46,
https://www.tandfonline.com/doi/abs/10.1080/01434632.1988.9994334.
5. Determine if learning: Don’t take this to mean that I think grad school is useless. The
important thing to decide is whether it will really matter to you, depending on the job you
want, the subject of your study, and the institution. My point isn’t that grad school is a
waste of time but rather that when making a decision involving so much time and cost,
you’d better do the research first!
6. For example, one common recommendation: Victor Mair, “How to Learn Chinese and
Japanese,” Language Log, February 17, 2014, http://languagelog.ldc.upenn.edu/nll/?
p=10554.
7. The literature on self-directed learning: George E. Spear and Donald W. Mocker, “The
Organizing Circumstance: Environmental Determinants in Self-Directed Learning,” Adult
Education Quarterly 35, no. 1 (March 1, 1984): 1–10,
https://journals.sagepub.com/doi/abs/10.1177/0001848184035001001?journalCode=aeqb.
8. That led me to do: “Portrait Drawing—The Complete Online Course,” Vitruvian Studio,
https://vitruvianstudio.com/course/portrait-drawing/.
Chapter V: Principle 2—Focus: Sharpen Your Knife
1. Somerville explained, “she would have been contented”: Mary Somerville, Personal
Recollections, from Early Life to Old Age, of Mary Somerville: With Selections from Her
Correspondence (London: Roberts Brothers, 1874), 23.
2. Hence, skilled performers: K. Anders Ericsson, The Road to Excellence: The Acquisition
of Expert Performance in the Arts and Sciences, Sports, and Games (New York:
Psychology Press, 2014), 25.
3. Similarly, the phenomenon: John Dunlosky, Katherine A. Rawson, Elizabeth J. Marsh, et
al., “Improving Students’ Learning with Effective Learning Techniques,” Psychological
Science in the Public Interest 14, no. 1 (January 8, 2013): 4–58,
https://journals.sagepub.com/doi/abs/10.11771529100612453266.
4. “learn to let it arise”: Susan L. Smalley and Diana Winston, Fully Present: The Science,
Art, and Practice of Mindfulness (Philadelphia: Da Capo Lifelong Books, 2010), 59.
5. High arousal creates: A. E. Bursill, “The Restriction of Peripheral Vision During
Exposure to Hot and Humid Conditions,” Quarterly Journal of Experimental Psychology
10, no. 3 (August 1, 1958): 113–29.
6. Too much arousal, however: This inverse-U shape of arousal versus performance is
known in psychology as the Yerkes-Dodson law.
7. More complex tasks: Daniel Kahneman, Attention and Effort (Englewood Cliffs, NJ:
Prentice-Hall), 1973.
8. When doing a particularly creative task: Kalina Christoff, Zachary C. Irving, Kieran C. R.
Fox, et al., “Mind-Wandering as Spontaneous Thought: A Dynamic Framework,” Nature
Reviews Neuroscience 17, no. 11 (2016): 718–31,
https://www.nature.com/articles/nrn.2016.113.
9. In one experiment, sleep-deprived: Robert T. Wilkinson, “Interaction of Noise with
Knowledge of Results and Sleep Deprivation,” Journal of Experimental Psychology 66,
no. 4 (November 1963): 332–37, https://psycnet.apa.org/record/1964–03490–001.
Chapter VI: Principle 3—Directness: Go Straight Ahead
1. Jaiswal leaves the offices: This is, in fact, the same Vatsal Jaiswal who joined me on my
yearlong language learning project in chapter 1. These events took place a few years prior
to that.
2. “Despite the importance”: Robert Haskell, Transfer of Learning (Cambridge, MA:
Academic Press, 2000), xiii.
3. In another study, college graduates were asked: James F. Voss, Jeffrey Blais, Mary L.
Means, Terry R. Greene, and Ellen Ahwesh, “Informal Reasoning and Subject Matter
Knowledge in the Solving of Economics Problems by Naive and Novice Individuals,”
Cognition and Instruction 3, no. 3 (1986): 269–302.
4. “in almost all the empirical work to date”: Michelene T. H. Chi and Miriam Bassok,
“Learning from Examples via Self-explanations,” Knowing, Learning, and Instruction:
Essays in Honor of Robert Glaser (1989): 251–82.
5. “students who receive honors grades”: Howard Gardner, The Unschooled Mind: How
Children Think and How Schools Should Teach, Basic Books (AZ), 2011.
6. “Researchers who rigorously evaluate training”: John H. Zenger, “Great Ideas Revisited.
The Painful Turnabout in Training. A Retrospective,” Training and Development 50, no.
1 (1996): 48–51.
7. “Transfer is paradoxical”: Wilbert J. McKeachie, “Cognitive Skills and Their Transfer:
Discussion,” International Journal of Educational Research 11, no. 6 (1987): 707–12.
8. Better graphics and sounds: Robert W. Proctor, and Addie Dutta, Skill Acquisition and
Human Performance (Thousand Oaks, CA: Sage Publications, 1995).
Chapter VII: Principle 4—Drill: Attack Your Weakest Point
1. However, it was in the latter half: Benjamin Franklin, The Autobiography of Benjamin
Franklin (New Haven, CT: Yale University Press, 2003).
2. world-changing consequences: Walter Isaacson, Benjamin Franklin: An American Life
(New York: Simon and Schuster, 2003).
3. “written equally well”: Ibid.
Chapter VIII: Principle 5—Retrieval: Test to Learn
1. What’s more, he claimed: Robert Kanigel, The Man Who Knew Infinity: A Life of the
Genius Ramanujan (New York: Simon and Schuster, 2016).
2. This is essentially the question: Jeffrey D. Karpicke, and Janell R. Blunt, “Retrieval
Practice Produces More Learning than Elaborative Studying with Concept Mapping,”
Science 331, no. 6018 (February 11, 2011): 772–75,
http://science.sciencemag.org/content/331/6818/772.
3. Minutes after studying something: Henry L. Roediger III and Jeffrey D. Karpicke, “The
Power of Testing Memory: Basic Research and Implications for Educational Practice,”
Perspectives on Psychological Science 1, no. 3 (September 1, 2006): 181–210,
https://journals.sagepub.com/doi/abs/10.1111/j.1745–6916.2006.00012.x?
journalCode=ppsa.
4. Inevitably, students who were performing: Jeffrey D. Karpicke, “Metacognitive Control
and Strategy Selection: Deciding to Practice Retrieval During Learning,” Journal of
Experimental Psychology: General 138, no. 4 (2009): 469–86,
http://memory.psych.purdue.edu/downloads/2009_Karpicke_JEPGeneral.pdf.
5. One answer comes: Robert A. Bjork, “Memory and Metamemory Considerations in the
Training of Human Beings,” in Metacognition: Knowing About Knowing, ed. J. Metcalfe
and A. Shimamura (Cambridge, MA: MIT Press, 1994): 185–205.
6. Delaying the first test: Jeffrey D. Karpicke and Henry L. Roediger III, “Expanding
Retrieval Practice Promotes Short-Term Retention, but Equally Spaced Retrieval
Enhances Long-Term Retention,” Journal of Experimental Psychology: Learning,
Memory, and Cognition 33, no. 4 (July 2007): 704–19,
http://memory.psych.purdue.edu/downloads/2007_Karpicke_Roediger_JEPLMC.pdf.
7. However, if you delay the test: Herbert F. Spitzer, “Studies in Retention,” Journal of
Educational Psychology 30, no. 9 (December 1939): 641–56,
https://www.gwern.net/docs/spacedrepetition/1939-spitzer.pdf.
8. An interesting observation: Chunliang Yang, “Enhancing Learning and Retrieval: The
Forward Testing Effect,” PhD diss., University College London, 2018.
Chapter IX: Principle 6—Feedback: Don’t Dodge the Punches
1. “It’s not going to be”: Kelefa Sanneh, “Chris Rock, the Duke of Doubt,” New Yorker,
November 10, 2014, https://www.newyorker.com/magazine/2014/11/10/duke-doubt.
2. Many medical practitioners get worse: Anders Ericsson and Robert Pool, Peak: Secrets
from the New Science of Expertise, (New York: Houghton Mifflin Harcourt, 2016).
3. In a large meta-analysis, Avraham Kluger: Avraham N. Kluger, and Angelo DeNisi, “The
Effects of Feedback Interventions on Performance: A Historical Review, a Meta-analysis,
and a Preliminary Feedback Intervention Theory,” Psychological Bulletin 119, no. 2
(1996): 254–84, https://psycnet.apa.org/record/1996–02773–003.
4. In one study, feedback: Michael H. Herzog and Manfred Fahle, “The Role of Feedback in
Learning a Vernier Discrimination Task,” Vision Research 37, no. 15 (August 1997):
2133–41, https://ac.els-cdn.com/S0042698997000436/1-s2.0-S0042698997000436-
main.pdf?_tid=9e63a472–9df4–43fa-a165–
7ff3daa4ddd2&acdnat=1551035784_e6ebf10b08703a5479c3abbf649b5320.
5. “The best feedback is informative”: Maria Araceli Ruiz-Primo and Susan M. Brookhart,
Using Feedback to Improve Learning (New York: Routledge, 2017), 128.
6. James A. Kulik and Chen-Lin C. Kulik review the literature: James A. Kulik and ChenLin C. Kulik, “Timing of Feedback and Verbal Learning,” Review of Educational
Research 58, no. 1 (1988): 79–97.
7. Expertise researcher: K. Anders Ericsson, Ralf T. Krampe, and Clemens Tesch-Römer,
“The Role of Deliberate Practice in the Acquisition of Expert Performance,”
Psychological Review 100, no. 3 (1993): 363–406, https://psycnet.apa.org/record/1993–
40718–001.
8. In those studies, however: Wendy Jaehnig and Matthew L. Miller, “Feedback Types in
Programmed Instruction: A Systematic Review,” Psychological Record 57, no. 2 (2007):
219–32.
Chapter X: Principle 7—Retention: Don’t Fill a Leaky Bucket
1. French, with its gendered nouns: Corazon Miller, “How Kiwi Nigel Richards Won French
Scrabble Championship,” New Zealand Herald, July 22, 2015,
https://www.nzherald.co.nz/lifestyle/news/article.cfm?c_id=6&objectid=11485116.
2. “Nigel, since you’re no good at words”: Zeba Sultan, “Nigel Richards—An Enigma,” The
paladin speaks . . . http://vivaciouspaladin.blogspot.com/2013/05/nigel-richardsan-
enigma.html.
3. “When I see you, I can never tell”: Stefan Fatsis, “Nigel Richards Article,” Scrabble
Study Log, http://scrabblestudylog.blogspot.com/2009/08/nigel-richards-article-by-stefanfatsis.html.
4. He politely declined: Tim Hume, “A Way with Words,” Sunday Star-Times, June 6, 2010,
http://www.stuff.co.nz/sunday-star-times/features/3778594/A-way-with-words.
5. “The cycling helps”: Fatsis, “Nigel Richards Article.”
6. “It’s hard work”: Daniel Stembridge, “Meeting Nigel Richards,” Mindsports Academy,
https://www.mindsportsacademy.com/Content/Details/2133?title=meeting-nigel-richards.
7. “I’m not sure there is a secret”: OgilvyBroadcast, “World Scrabble Championships 2011,”
filmed October 2011, YouTube video, 1:51, posted October 2011,
https://www.youtube.com/watch?v=EZE_olsi-pM&t=1m46s.
8. “Physicians with more experience”: Niteesh K. Choudhry, Robert H. Fletcher, and
Stephen B. Soumerai, “Systematic Review: The Relationship Between Clinical
Experience and Quality of Health Care,” Annals of Internal Medicine 142, no. 4 (2005):
260–73, https://annals.org/aim/fullarticle/718215/systematic-review-relationshipbetween-clinical-experience-quality-health-care.
9. This seems especially likely: Joyce W. Lacy and Craig E. L. Stark, “The Neuroscience of
Memory: Implications for the Courtroom.” Nature Reviews Neuroscience 14, no. 9
(September 2013): 649–58, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4183265/.
10. The authors of a popular study guide: Peter Wei and Alex Chamessian, Learning
Medicine: An Evidence-Based Guide (Self-published, 2015).
11. procedural skills, such as: Jong W. Kim, Frank E. Ritter, and Richard J. Koubek, “An
Integrated Theory for Improved Skill Acquisition and Retention in the Three Stages of
Learning,” Theoretical Issues in Ergonomics Science 14, no. 1 (2013): 22–37.
12. Overlearning is a well-studied: James E. Driskell, Ruth P. Willis, and Carolyn Copper,
“Effect of Overlearning on Retention,” Journal of Applied Psychology 77, no. 5 (1992):
615–22, https://psycnet.apa.org/record/1993–04376–001.
13. One study of algebra students: Harry P. Bahrick and Lynda K. Hall, “Lifetime
Maintenance of High School Mathematics Content,” Journal of Experimental
Psychology: General 120, no. 1 (1991): 20–33,
http://citeseerx.ist.psu.edu/viewdoc/download?
doi=10.1.1.1020.7785&rep=rep1&type=pdf.
14. Rajveer Meena, the Guinness World Record: “Most Pi Places Memorised,” Guiness
World Records, http://www.guinnessworldrecords.com/world-records/most-pi-placesmemorised.
Chapter XI: Principle 8—Intuition: Dig Deep Before Building Up
1. “a magician of the highest caliber”: James Gleick, Genius: The Life and Science of
Richard Feynman (New York: Vintage, 1993), 10.
2. “He’s the only guy”: Richard P. Feynman and Ralph Leighton, “Surely You’re Joking,
Mr. Feynman!”: Adventures of a Curious Character (New York: Random House, 1992),
133.
3. “I happened to know”: Ibid., p. 193.
4. “I had a scheme”: Ibid., p. 85.
5. In a famous study, advanced PhDs: Michelene T. H. Chi, Paul J. Feltovich, and Robert
Glaser, “Categorization and Representation of Physics Problems by Experts and
Novices,” Cognitive Science 5, no. 2 (April 1981): 121–52,
https://onlinelibrary.wiley.com/doi/pdf/10.1207/s15516709cog0502_2.
6. Another study, this time: William G. Chase and Herbert A. Simon, “Perception in Chess,”
Cognitive Psychology 4, no. 1 (January 1973): 55–81,
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.601.2724&rep=rep1&type=pdf.
7. Researchers have estimated: Fernand Gobet and Herbert A. Simon, “Expert Chess
Memory: Revisiting the Chunking Hypothesis,” Memory 6, no. 3 (1998): 225–55,
https://pdfs.semanticscholar.org/d11f/079a1d6d3147abbb7868955a6231f4a5ba5b.pdf.
8. “If [he] had said”: Feynman and Leighton, “Surely You’re Joking, Mr. Feynman!,” 21.
9. Feynman told a story: The work, which won the pair the Nobel Prize, demonstrated that
the universe we live in is not mirror-image symmetrical. That is to say, there are certain
physical processes that look different in a mirror version. At the time, it was an enormous
surprise to physicists, who had assumed that this symmetry existed. Ibid., 249.
10. One of Einstein’s earliest: Walter Isaacson, Einstein: His Life and Universe (New York:
Simon and Schuster, 2008).
11. “illusion of explanatory depth”: Rebecca Lawson, “The Science of Cycology: Failures to
Understand How Everyday Objects Work,” Memory & Cognition 34, no. 8 (2006): 1667–
75, http://gearinches.com/misc/science-of-cycology.PDF.
12. Feynman’s and Einstein’s approach: The artist and designer Gianluca Gimini plays on this
concept by designing bicycles that look as people think they ought to (but that of course
don’t work). You can see some of his creations at
gianlucagimini.it/prototypes/velocipedia.html.
13. In one study of this effect: Fergus I. M. Craik and Robert S. Lockhart, “Levels of
Processing: A Framework for Memory Research,” Journal of Verbal Learning and Verbal
Behavior 11, no. 6 (December 1972): 671–84,
http://wixtedlab.ucsd.edu/publications/Psych%20218/Craik_Lockhart_1972.pdf.
14. Those who processed the words: Thomas S. Hyde and James J. Jenkins, “Differential
Effects of Incidental Tasks on the Organization of Recall of a List of Highly Associated
Words,” Journal of Experimental Psychology 82, no. 3 (1969): 472–81,
https://people.southwestern.edu/~giuliant/LOP_PDF/Hyde1969.pdf.
15. The Dunning-Kruger effect occurs: Justin Kruger and David Dunning, “Unskilled and
Unaware of It: How Difficulties in Recognizing One’s Own Incompetence Lead to
Inflated Self-Assessments,” Journal of Personality and Social Psychology 77, no. 6
(December 1999): 1121–34,
https://pdfs.semanticscholar.org/e320/9ca64cbed9a441e55568797cbd3683cf7f8c.pdf.
16. “Some people think”: Feynman and Leighton, “Surely You’re Joking, Mr. Feynman!,”
244.
17. I had this uneasy feeling: Ibid., 281.
18. With my textbook at my side: You can view my notes here:
https://www.scotthyoung.com/mit/photogrammetry.pdf.
19. To get a better handle: You can view my notes here:
https://www.scotthyoung.com/mit/grid-accel.pdf.
20. “I got it down”: Ibid., 141.
Chapter XII: Principle 9—Experimentation: Explore Outside Your
Comfort Zone
1. “You started too late”: Steven W. Naifeh and Gregory White Smith, Van Gogh: The Life
(New York: Random House, 2011), 260.
2. “We considered his work”: Ibid., 514.
3. sold for more than $82 million: Judd Tully, “$82.5 Million for van Gogh; Japanese Buyer
Sets Art Auction Record,” http://juddtully.net/auctions/82–5-million-for-van-goghjapanese-buyer-sets-art-auction-record/.
4. “devoured these big books”: Naifeh and Smith, Van Gogh, 214.
5. “Scarcely any color is not gray”: Ibid., 333.
6. growth mindset: Carol S. Dweck, Mindset: The New Psychology of Success (New York:
Random House, 2008).
7. It may also dispel: I had my own experience when trying to write this book. As part of my
process, I reread many other books whose style I wanted to emulate. In doing this, a thing
that surprised me was that many such books had far fewer citations than I remembered,
the “seriousness” of a book being mostly a matter of tone, not of scholarship.
8. Scott Adams, the creator of Dilbert: Scott Adams, “Career Advice,” Dilbert.Blog, July 20,
2007, http://dilbertblog.typepad.com/the_dilbert_blog/2007/07/career-advice.html.
Chapter XIV: An Unconventional Education
1. “Grandmasters don’t like to lose”: The source I’ve been able to track down seems to be
here: Shelby Lyman (02–08–1987), “Younger Sisters Are Also Proficient,” Sunday
Telegraph 1 (45).
2. “three or four great chess prodigies in history”: F. Lidz, “Kid with a Killer Game,” Sports
Illustrated 72, no. 6 (1990): 8–8.
3. “She has fantastic chess talent”: Ibid.
4. “I was playing the World Champion”: Chess Life 50, (no. 7–12): 647.
5. “How could you do this to me?”: Leonard Barden, “Sweet Revenge for Kasparov’s
Opponent,” Guardian, September 11, 2002,
https://www.theguardian.com/world/2002/sep/11/3.
6. “I think a girl of her age”: Dirk Jan ten Geuzendam, “Finding Bobby Fischer: Chess
Interviews by Dirk Jan ten Geuzendam,” Alkmaar, the Netherlands: New in Chess (1994),
203.
7. “A genius is not born”: Peter Maass, “Home-Grown Grandmasters,” Washington Post,
March 1992.
8. “[W]hen I looked at the stories”: Linnet Myers, “Trained to Be a Genius, Girl, 16,
Wallops Chess Champ Spassky for $110,000,” Chicago Tribune, February 1993.
9. “Women are able”: Patricia Koza, “Sisters Test Male Domination of Chess,” Mohave
Daily Miner, November 1986.
10. “The Polgárs showed”: G. K. Kasparov and Mig Greengard, How Life Imitates Chess:
Making the Right Moves, from the Board to the Boardroom (New York: Bloomsbury,
2008).
11. “Have they been educated”: László Polgár, Raise a Genius! (Vancouver: self-published,
2007), 97, https://docplayer.net/64270951-Raise-a-genius-by-laszlo-polgar-originaledition-laszlo-polgar-nevelj-zsenit-budapest-interviewer-endre-farkas.html.
12. “starting from 4–5”: Ibid., 33.
13. “play is not the opposite”: Ibid., 20.
14. “Following a number”: Ibid., 16.
15. “We should make”: Ibid., 51.
16. “One thing is certain”: Ibid.
17. “one of the most important”: Ibid., 36.
18. “opening preparation was not”: Judit Polgár, How I Beat Fischer’s Record (Glasgow:
Quality Chess UK Ltd, 2012), 11.
Index
The pagination of this digital edition does not match the print edition from which the index was created. To
locate a specific entry, please use your ebook reader's search tools.
Adams, Scott, 211
American Revolution, 107
analytics, 10–11
Anki, 165–66
arousal, task complexity and, 83–85
artistic skills, intensive practice of, x–xi, xii, 19–20
Atomic Habits (Clear), xiii
Autobiography (Franklin), 107–8, 118
Autor, David, 28
Barone, Eric, 12–15, 21, 23, 33, 91, 93, 117, 118, 139, 142
benchmarks, benchmarking, 64–65, 218
Bishop, Errett, 177
Bjork, R. A., 125
Blunt, Janell, 122–23, 132
Bohr, Niels, 178
Brookhart, Susan M., 144
careers, ultralearning and:
acceleration of, 32
changing of, 32, 45–46
and cultivation of hidden advantages, 32, 33
Carr, George Shoobridge, 121, 123, 133
challenges, self-generated, 132
chess:
pattern recognition in, 182–83
Polgár sisters and, 233–49
sexism in, 234, 237–38
Chi, Michelene, 95
Chi, Michelene, 95
children:
and pressure to achieve, 241–42
ultralearning and, 241–49
Cirillo, Francesco, 76n
cognitive components, in drills, 115
cognitive load, drill and, 110–11, 116
cognitive science, 23, 49
college degrees:
cost vs. practical value of, 5, 29–30, 59; see also education, traditional
Comedy Cellar, 136
competition, self-confidence and, 244, 251–53
concept mapping, 122–23, 124, 132–33
concepts, in metalearning, 62
copying, in drills, 115–16
corrective feedback, 141, 144–45
Cowen, Tyler, 27–28
Craig, Roger, 9–12, 21, 23, 33, 91, 93, 99–100, 137, 163, 164, 165, 229, 251
Csíkszentmihályi, Mihály, 78
cycling, 156–57
Dalí, Salvador, 197–98
Davy, Humphrey, 107
Declaration of Independence, 107
Deep Blue chess computer, 234
deliberate practice, 78, 109, 137–38
de Montebello, Tristan, 40–47, 104, 137, 142, 143, 144–45, 146, 151, 251, 252
DeNisi, Angelo, 138–39
difficulty, desirable, 215–16
directness (ultralearning principle), xi, xiii, 48, 87–105, 222, 246
contextual emphasis of, 90, 91, 97–99
difficulty of, 101
drill and, 111–13
feedback and, 144
immersive approach in, 102–3
indirect learning vs., 91, 105
overkill approach to, 104–5, 171
project-based learning and, 101–2
retrieval and, 128–29
simulation in, 103
skill sets and, 88–90
transfer and, 97–101
direct practice, xi, 157, 218
Direct-Then-Drill Approach, 112–13, 114
distractions, 77
and choice of learning tools, 81–82
environment and, 80–81
environment and, 80–81
mental, 82–83
drill (ultralearning principle), xiii, 48, 106–18, 223, 246–47
cognitive components in, 115
cognitive load and, 110–11, 116
copying in, 115–16
directness and, 111–13
effective, difficulty of designing, 113–14
Magnifying Glass Method in, 116
prerequisite chaining in, 116–17
rate-determining steps and, 110–11
time slicing in, 114–15
in ultralearning vs. traditional learning, 117–18
Dunning-Kruger effect, 190
Duolingo, 91–92, 165
Durant, Colby, 32
Dweck, Carol, 207
Ebbinghaus, Hermann, 159–60
“Edict by the King of Russia, An” (Franklin), 107
Edison, Thomas, 197
education, traditional:
failure of transfer in, 94–97
indirect learning in, 97
practical skills downplayed in, 87–88, 90, 91, 93–94
value of, 231–32; see also college degrees
effectiveness, prioritizing of, 17, 26, 29, 36, 37–39, 48, 49
efficiency, maximizing of, 10, 11, 32, 38, 39, 49, 127–28
Einstein, Albert, 72, 186–87
emotions, as source of distraction, 82–83
Emphasize/Exclude Method, 65–66
entrepreneurs, entrepreneurship, xii–xiii
Erdős, Paul, 72
Ericsson, K. Anders, 36, 78, 137, 147, 183n
Euler, Leonhard, 70
“Eureka!” moments, 84
Everett, Dan, 51–55, 101
experimentation (ultralearning principle), 49, 197–215, 223, 248–49
combining skills in, 211
comparing methods in, 209–10
copying in, 208–9
extremes and, 211–12
introducing new constraints in, 210–11
learning resources and, 204–5
mastery and, 203–4
mindset and, 207–8, 213–14
mindset and, 207–8, 213–14
with style, 206
tactics for, 208–12
with techniques, 205–6
uncertainty and, 213–15
facts, in metalearning, 62
Farkas, Endre, 240
Fédération Internationale des Échecs (FIDE) (World Chess Federation), 233
feedback (ultralearning principle), xiii, 48, 135–52, 223, 247
corrective, 141, 144–45
difficulty of task and, 149
fear of, 140, 151
filtering and, 148–49
high-intensity, 150–51, 152
immediate vs. delayed, 146–47
informational, 141, 143–44
meta-, 150
outcome, 141–43
positive vs. negative impact of, 138–40
retrieval and, 147
self-provided, 143–44
self-testing and, 123
signal vs. noise in, 148–49, 152
underuse of, 140
Fehsenfeld, Diana, 33
Felt, Bob, 155
Feynman, Richard, 177–81, 193–94, 195–96
Feynman Technique, 191–95
filtering, feedback and, 148–49
Fischer, Bobby, 233, 235
five-minute rule, 75–76, 77
flash cards, 130, 166
flow, 78–79
focus (ultralearning principle), xiii, 48, 70–86, 222, 246
arousal vs. task complexity in, 83–85
failing to sustain, 77–83; see also distractions
optimal length of, 79–80
procrastinating and, see procrastinating
schedules and, 76–77
Foer, Joshua, 174
fooling oneself, understanding and, 187–88, 190–91, 192
forgetting, 159–63, 175–76
see also retention
formal discipline theory, 95–96
forward-testing effect, 126–27
forward-testing effect, 126–27
Fowler, Trent, 22
Franklin, Benjamin, 106–9, 111, 114, 115, 118
free (active) recall, 121–23, 125, 131, 157, 166, 176
games (video), 12–15
Gauguin, Paul, 201, 206
Gendler, Michael, 42–45, 144–45
general theory of relativity, 72
genes, intelligence and, 36
genius, 34–35, 177, 179
genetics and, 240
L. Polgár's theory of, 236, 239–40
Gleick, James, 179
globalization, 28
Graham, Paul, xiv
habits, low-intensity, learning and, 229–31
Hardy, G. H., 119–20, 121
Harvest Moon (video game), 12
Haskell, Robert, 94, 96–97, 100
Herschel, Caroline, 71
humility, learning and, 256
Hungarian Chess Federation, 237
hyperpolyglots, 9
illusion of explanatory depth, 187–88, 192
income inequality, 28
“Influence of Improvement in One Mental Function upon the Efficiency of Other Functions,
The” (Thorndike and Woodworth), 95–96
informational feedback, 141, 143–44
instrumental learning projects, 58
intelligence, genes and, 36
interference, retention and, 161–62
intrinsic learning projects, 58–59
intuition (ultralearning principle), 49, 177–96, 223, 248
demystifying of, 195–96
Feynman Technique for developing, 191–95
problem solving and, see problem solving
italki, 166
Jaiswal, Vatsal, 87–90, 93
James, William, 119
Jaunzeikare, Diana, 21–22
Jefferson, Thomas, 107
Jeopardy! (TV show), 9–12, 33, 91, 93, 99–100, 137, 163, 165, 229, 251
Johnston, Philip, 106
judgments of learning (JOLs), 124
judgments of learning (JOLs), 124
Kac, Mark, 177
Karpicke, Jeffrey, 122–23, 124, 132
Kasparov, Garry:
initial sexism of, 234
J. Polgár's first match with, 234–35
J. Polgár's rematch with, 238–39
keyword mnemonic method, 173–74
Kluger, Avraham, 138–39
knowledge, unlimited expansion of, 256–57
Kulik, Chen-Lin C., 146–47
Kulik, James A., 146–47
language learning, 5–9, 209–10
immersive, ix, x, 51–53, 56, 91, 92, 102
metalearning and, 53–54, 55–56
procedural memory in, 169
Laplace, Pierre-Simon, 71, 72
Latin, study of, 96
Law of Diminishing Returns, 68
Lawson, Rebecca, 187–88
learning:
asking questions and, 190–91
coercive, 244
concept mapping and, 122–23, 124, 132–33
creating inspiring goals in, 250–51
by doing, xi, 92–93, 99; see also directness
free (active) recall and, 121–23, 131
humility and, 256
immersive, 17–19, 102–3; see also language learning, immersive
indirect approaches to, 91–92, 97, 101, 105
lifelong, 232
low-intensity habits for, 229–31
passive review and, 121–22, 124, 130
prioritizing of, 253–54
project-based, 101–2
rate-determining steps in, 109–11, 112, 113
research on, 254–57
retrieval and, see retrieval
role play in, 243–44
self-confidence and, xiv, 256
technology and, 5, 29–32
transfer and, see transfer
see also ultralearning, ultralearners
Lee, T. D., 186, 187
Lee, T. D., 186, 187
Leonardo da Vinci, 87, 197
levels-of-processing effect, 189–90
Lewis, Benny, 5–9, 17, 21, 33–34, 40, 91, 92, 104–5, 117, 125–26, 137, 151, 163, 229
lifelong learning, 232
Littlewood, John, 120
McKeachie, Wilbert, 96
Magnifying Glass Method, 116
Maini, Vishal, 32, 100
Mair, Victor, 66
Manhattan Project, 178
Massachusetts Institute of Technology (MIT), online course of, 1–5
mastery:
experimentation and, 203–4
ultralearners and, 100–101
mathematics, 1–2, 22, 63, 109
Feynman and, 177–78, 179, 180, 181
Ramanujan and, 119–21, 123
Somerville and, 71–73
Tao and, 34–36
Mauve, Anton, 201
Mednis, Edmar, 234
Meena, Rajveer, 173
memory, see retention
Memrise, 165
metafeedback, 150
metalearning (ultralearning principle), xiii, 48, 51–69, 203, 217, 222, 245–46
definition of, 53
expert interviews in, 59–60
“How?” questions in, 58, 64–65
long-term effects of, 57, 68–69
as mapmaking, 54–56
planning time in, 66–68
short-term strategies for, 56–69
“What?” questions in, 58, 61–64
“Why?” questions in, 57–61
metalinguistic awareness, 55
mindset:
experimentation and, 207–8
fixed vs. growth, 207, 213–14
MIT Challenge, x, 2–5, 15–16, 67, 92, 93, 144, 167, 169, 171, 250, 261–63
mnemonics, 172–75, 209–10, 214
monolingual fieldwork, 54
Moonwalking with Einstein: The Art and Science of Remembering Everything (Foer), 174
multitasking, 81
Naifeh, Steven, 199
Nemitrmansuk, Pakorn, 156
Newton, Isaac, 51
noise, signal vs., 148–49, 152
outcome feedback, 141–43
overlearning, 104–5, 170–72
pattern recognition, 182–83, 184
Pavlina, Steve, 21
photography, x–xi, xii
physics, 178, 180, 181, 182
Picasso, Pablo, 197
Pike, Kenneth, 54
Pimsleur, 165
pixel art, 13–14, 117
planning, allotting time for, 66–68
play, in learning, 243–44
Polgár, Judit, 233–49
chess as obsession of, 238
chess education of, 235–38
Kasparov's first match with, 234–35
Kasparov's rematch with, 238–39
Polgár, Klára, 236, 237
Polgár, László, 237, 242
coercive learning opposed by, 244
daughters' chess education as focus of, 236–37, 243
daughters' general education and, 237
educational strategy of, 242–49
genius as viewed by, 236, 239–40
play emphasized by, 243–44
positive reinforcement practiced by, 244
sexism opposed by, 237–38
ultralearning principles used by, 245–49
Polgár, Zsófia, 236, 238
chess education of, 237
Polgár, Zsuzsa, 236, 237, 238
chess education of, 237
Pomodoro Technique, 76, 77
Poor Richard’s Almanack (Franklin), 106, 108–9
Portrait Drawing Challenge, 265
positive reinforcement, 244
praise, negative impact of, 138–39
prerequisite chaining, 116–17
problem solving:
concrete examples in, 189–90
concrete examples in, 189–90
not giving up on, 185–86
pattern recognition in, 182–83, 184
principles-first approach to, 182, 183–84
proving and, 186–87
proceduralizations, 167–70
procedures, in metalearning, 62–63
processing, levels of, 189–90
procrastinating, 73–77
causes of, 74
self-recognition of, 74–75
tactics for combating, 75–77
proving, developing understanding through, 186–88
public speaking, ultralearning and, 41–46
Putnam Mathematics Competition, 178
Pythagorean Theorem, 187
questions, importance of asking, 190–91
Raise a Genius! (Polgár), 240, 242
Ramanujan, Srinivasa, 119–21, 123, 133
Rappard, Anthon van, 201, 206
rate-determining steps, 109–11, 112, 113
drill and, 110–11
recall, free (active), 121–23, 125, 131, 157, 166, 176
Reddit, 16
reference groups, ultralearning and, 252–53
regionalization, 28–29
remembering, see retention; retrieval
research:
allotting time for, 66–68
for ultralearning projects, 217–19
resources, choosing, 218
retention (ultralearning principle), 48, 153–76, 223, 248
decay over time of, 160–61
difficulty of, 159–63
false memories and, 163
interference and, 161–62
lost cues and, 162–63
mechanisms for, 163–75
mnemonics and, 172–75
overlearning and, 170–72
proceduralization in, 167–70
spacing and, 164–65
retrieval (ultralearning principle), 48, 119–34, 223, 247–48
closed-book tactic in, 132–33
desirable difficulty in, 125–26
desirable difficulty in, 125–26
directness and, 128–29
feedback and, 147
flash cards and, 130
forward-testing effect in, 126–27
free (active) recall and, 121–22, 131, 157, 166, 176
maximizing efficiency in, 127–28
question-book technique in, 131–32
self-generated challenges in, 132
Revit, 89
Richards, Nigel, 153–59, 164, 166, 176
Rock, Chris, 135–36, 140, 143
Royal Astronomical Society, 71
Ruiz-Primo, Maria Araceli, 144
schedules, 76–77
Scrabble:
French-language version of, 153–54
rules and strategies of, 154–56
self-confidence:
competition and, 244, 251–53
learning and, xiv, 256
self-education:
directness and, 90
intense, see ultralearning, ultralearners
of van Gogh, 200–203, 204–5, 206
self-testing, 121–23
feedback and, 123
see also retrieval
Shelley, Mary, 216
Short, Nigel, 234
signal, noise vs., 148–49, 152
skill polarization, 28
skill sets, acquiring and upgrading of, 88–90
Smalley, Susan, 83
Smith, Gregory White, 199
Socratic method, 108
Somerville, Mary, 70–73, 77, 85, 86
spaced-repetition systems (SRS), 11, 165–66, 175
spacing, retention and, 164–65
Spectator, 107–8
Stardew Valley (video game), 15
subject matter, mastery of, 100–101
Sujjayakorn, Panupol, 156
Synopsis of Elementary Results in Pure and Applied Mathematics, A (Carr), 121, 123
Tal, Mikhail, 234
Tal, Mikhail, 234
talent, ultralearning and, 34–37
Tamu (username), 22
Tao, Terence, 34–35
task complexity, arousal and, 83–85
technology:
learning and, 5, 29–32
work and, 28–29
10 Percent Rule, 66–67, 68
Tersteeg, H. G., 198
Thorndike, Edward, 95–96
time slicing, 114–15
Toastmasters International, 42–45
topics, choosing of, 217–18
Toulouse-Lautrec, Henri de, 198
Traité de mécanique céleste (Laplace), 71, 72
transfer, 94–99
directness and, 97–101
failure of traditional education to achieve, 94–97
Tyson, Mike, 135
ultralearning, ultralearners, 9, 21
alternatives to, 228–32
careers and, see careers, ultralearning and
children and, 241–49
definition of, 25–26
ethos of, 49–50, 158
everyday application of, 23–24
fostering of, 249–54
instrumental vs. intrinsic projects in, 58–59
as life changing, 45–46
L. Polgár's strategy for, 242–49
mastery and, 100–101
obsessive intensity of, 26, 39, 133–34, 157, 158, 176, 202, 241
personal stories of, 2–24, 40–47, 51–55, 70–73, 87–89, 106–9, 119–21, 153–59, 177–81,
183–86, 193–94, 195–96, 197–98, 233–49, 255
principles of, xiii, 47–50, 245–49; see also specific principles
process of, 40–50
public speaking and, 41–46
reference groups and, 252–53
as self-directed, x, xiv, 25, 39, 49–50, 145, 231, 241
sense of self as expanded by, xiv–xv, 33–34
shared traits of, 22–23, 26, 33–34
as strategy, 25, 228
tactics of, 12, 21, 38, 47, 49
talent and, 34–37
talent and, 34–37
time commitment and, 37–38
value of, 26–27, 39
ultralearning projects, 216–32
applying principles and measuring progress in, 221–24
effort required by, 216–17
instrumental vs. intrinsic, 58–59
maintaining knowledge gained through, 225–26
mastery of skills gained through, 227–28
relearning knowledge gained through, 227
research for, 217–19
reviewing results of, 224–25
scheduling time for, 219–21
work ethic of, x, 46
UltraSpeaking, 45
uncertainty, experimentation and, 213–15
understanding:
illusion of, 187–88, 190–91, 192
proving as path to, 186–88
Unschooled Mind, The (Gardner), 95
van Gogh, Vincent, 197–98, 200–203, 204–5, 206
video games, 12–15
Vitruvian Studio, 171
Watson, John, 233
Willingham, Daniel, 153
Winston, Diana, 83
Woodworth, Robert, 95–96
work, technology and, 28–29
work ethic, 46
World Championship of Public Speaking, 43–45, 104
World Chess Championship, 233–34
World Scrabble Championships, 153, 156
Woźniak, Piotr, 11
Yang, C. N., 186, 187
“Year Without English, The,” ix, 18–19, 263–65
Zenger, John H., 95
About the Author
SCOTT H. YOUNG is a writer who undertakes interesting self-education
projects, such as attempting to learn MIT’s four-year computer science
curriculum in twelve months and learning four languages in one year. He lives in
Vancouver, Canada.
Discover great authors, exclusive offers, and more at hc.com.

Copyright
ULTRALEARNING. Copyright © 2019 by ScottHYoung.com Services Ltd. Foreword copyright © 2019
by James Clear. All rights reserved under International and Pan-American Copyright Conventions. By
payment of the required fees, you have been granted the nonexclusive, nontransferable right to access and
read the text of this e-book on-screen. No part of this text may be reproduced, transmitted, downloaded,
decompiled, reverse-engineered, or stored in or introduced into any information storage and retrieval
system, in any form or by any means, whether electronic or mechanical, now known or hereafter invented,
without the express written permission of HarperCollins e-books.
FIRST EDITION
Cover design by Caroline Johnson
Cover illustration © Davedka/Shutterstock
Illustrations in Chapter I courtesy of the author
Illustrations in Chapter XI © Rebecca Lawson
Library of Congress Cataloging-in-Publication Data has been applied for.
Digital Edition AUGUST 2019 ISBN: 978-0-06-285274-8
Print ISBN: 978-0-06-285268-7
About the Publisher
Australia
HarperCollins Publishers Australia Pty. Ltd.
Level 13, 201 Elizabeth Street
Sydney, NSW 2000, Australia
www.harpercollins.com.au
Canada
HarperCollins Publishers Ltd
Bay Adelaide Centre, East Tower
22 Adelaide Street West, 41st Floor
Toronto, Ontario, M5H 4E3
www.harpercollins.ca
India
HarperCollins India
A 75, Sector 57
Noida
Uttar Pradesh 201 301
www.harpercollins.co.in
New Zealand
HarperCollins Publishers New Zealand
Unit D1, 63 Apollo Drive
Rosedale 0632
Auckland, New Zealand
www.harpercollins.co.nz
United Kingdom
HarperCollins Publishers Ltd.
1 London Bridge Street
1 London Bridge Street
London SE1 9GF, UK
www.harpercollins.co.uk
United States
HarperCollins Publishers Inc.
195 Broadway
New York, NY 10007
www.harpercollins.com
* Credit for this quote has been given to a handful of people over the years,
but I believe the earliest source is from 1882 when a student named Benjamin
Brewster wrote in the Yale Literature Magazine, “I heard no more, for I was lost
in self-reproach that I had been the victim of ‘vulgar error.’ But afterwards, a
kind of haunting doubt came over me. What does his lucid explanation amount
to but this, that in theory there is no difference between theory and practice,
while in practice there is?”
* Paul Graham, “How to Be an Expert in a Changing World,” December
2014, http://www.paulgraham.com/ecw.html?viewfullsite=1.
* Technically, the term ultralearning was first used by Cal Newport, in his
headline to an article I wrote for his website about my recently completed MIT
Challenge, which he titled “Mastering Linear Algebra in 10 Days: Astounding
Experiments in Ultra-Learning.”
* The language of the speaker, it turned out, was a dialect of Hmong, spoken
in parts of China, Vietnam, and Laos.
* For our purposes, the terms metalinguistic awareness and metalearning are
interchangeable. The literature is replete with meta-terms (metaknowledge,
metacognition, metamemory, meta-metacognition, etc.) that have related usages.
* Calls on the phone might also avoid unwanted side effects of face-to-face
meetings. Women who have tried this method have told me that occasionally
their interviewee misinterpret their desire for learning advice as a date.
* This time management method comes from an Italian management
consultant, Francesco Cirillo. It is so named because pomodoro is Italian for
“tomato,” and the timer he used was shaped like a tomato.
* Directness, as I’m writing about here, is closely related to the concept of
transfer-appropriate processing, from psychological literature.
* In fairness to Duolingo, there are ways of using the app to get more direct
forms of practice, but these tend to come only from repeatedly practicing the
same lessons on the mobile version of the app.
* Modal logic is an extension of propositional logic, allowing you to express
ideas such as “should,” “usually,” or “possibly.”
* It should be noted that not all researchers agree with the chunking model. K.
Anders Ericsson, the psychologist behind deliberate practice, prefers an
alternative model called “Long-Term Working Memory.” The differences are
largely technical, and both models point to the idea of expertise being gained
through extensive context-specific practice.
* Calling this the Feynman Technique was possibly unwise. It’s unclear if
Feynman ever used this exact method, so I may have inadvertently given the
technique an illustrious history it doesn’t possess. In addition, one of Feynman’s
great contributions to physics was in the form of “Feynman Diagrams.” So, the
Feynman Technique can lead to diagrams, although not necessarily Feynman
Diagrams!
* This book, Raise a Genius!, originally appeared under the title Neveli zsenit!
I’m indebted to the blogger Scott Alexander and his readers for sourcing a
translation in English.