Ultralearning: Master Hard Skills, Outsmart the Competition, and Accelerate Your Career

by Scott H. Young

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.

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. 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.

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.

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.

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.

Forgetting is the default, not the exception, so the ultralearners I encountered had devised various strategies for coping with this fact of life.

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.

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 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 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

This difference between knowing how and knowing that may also have different implications for long-term memory. Procedural skills, such as the ever-remembered bicycling, are much less susceptible to being forgotten than knowledge that requires explicit recall to retrieve.11

One common, and useful, mnemonic is known as the keyword method.

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.

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.

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

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

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.

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. The method is quite simple: Write down the concept or problem you want to understand at the top of a piece of paper. In the space below, explain the idea as if you had to teach it to someone else. If it’s a concept, ask yourself how you would convey the idea to someone who has never heard of it before. If it’s a problem, explain how to solve it and—crucially—why that solution procedure makes sense to you. 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.

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.

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 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

1. Experimenting with Learning Resources The first place to experiment is with the methods, materials, and resources you use to learn.

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?”

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.

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

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

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.

Tactic 4: Find Your Superpower in the Hybrid of Unrelated Skills

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.

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.

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:

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 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.

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 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.

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-and-fast rule.

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.

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.

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:

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.