Scott H. Young's Blog, page 9

Get Better at Anything is Now Available

My new book, Get Better at Anything: 12 Maxims for Mastery, is now available!

Buy now:

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Life depends on learning. We spend decades in school, acquiring an education. We want to be good at our jobs, not just for the perks that come with being one of the best, but for the pride of mastering a craft. Even things we do for fun, we enjoy to a large extent because we feel capable of getting better at them.

Yet learning is often mysterious. 

Sometimes we improve effortlessly—in other cases it’s a slog. We can spend decades driving a car, hitting a tennis ball or working at our jobs without getting better at any of them.

In my new book, I explore the science of skill acquisition, illustrating the core principles that can help you get better at the things that matter most to you.

The Joy (and Frustration) of Trying to Get Better

Few experiences can top the joy of finally getting the hang of a new skill. The first time you have a successful conversation in a new language, solve a tricky programming problem or ski down a hill without falling is thrilling.

But, just as often, learning is frustrating. 

We can spend hours in the library and not understand a subject any better than when we began. We can feel stuck in our jobs and careers, unable to make the jump to something better. Sometimes, we even convince ourselves that entire fields are off-limits—that we’re just not capable of getting better.

Frustrations can come at the beginning—when we have to learn something new and don’t know where to start. It can be daunting to embark on a new profession, pick up a new hobby or take on projects with new responsibilities.

For other skills, the frustrations can occur later—when we have already spent hundreds of hours striving to improve and feel stalled or stuck. It can be discouraging to feel trapped in a career that’s hit a dead-end, or to have a golf handicap that isn’t getting any better no matter what we try. 

The key to transforming frustration to enthusiasm, to go from feeling stuck to making progress, is developing a deep understanding of how learning works. 

Get Better at Anything breaks down the process of learning into three fundamental ingredients, with twelve memorable maxims encapsulating the key concepts you need to make progress.

The Three Ingredients for Getting Better

Three factors underpin our ability to learn:

See. Most of what we know comes from other people. The ease (or difficulty) of learning from others explains much of our ability to improve ourselves.Do. Practice is essential to progress, but not all efforts are equal. The brain is a fantastic effort-saving machine, which is both a blessing and a curse. Knowing what kinds of actions lead to progress (and which don’t) can save years of wasted effort.Feedback. Improvement is not a straight line; it requires adjustment. Sometimes, feedback looks like the red stroke of a teacher’s pen, but more often, it comes from direct contact with the reality we’re engaged with.

Engaging in practice loops, where we see examples, practice for ourselves and get high-quality feedback is a proven method that has been used to accelerate progress in skills from novel writing to pilot training.

Details matter, of course. Which is why, in the book, I divide the three factors into distinct chapters, each introducing a central concept drawn from the research, backed with clear examples and practical applications for getting those details right. The twelve maxims for mastery are:

Problem Solving is Search. I’ll share how a new understanding of how people solve problems sheds light on the process of acquiring complex skills.Creativity Begins with Copying. Imitation isn’t the enemy of originality, but an important precursor to it. I’ll cover research showing why we can sometimes solve problems without learning how we solve them, as well as practical implications of one of the most celebrated findings in psychology.Success is the Best Teacher. Motivation starts from having the proper foundation. The concept of self-efficacy, pioneered by psychologist Albert Bandura, can explain why we sometimes feel driven to learn and other times shrink from new challenges.Knowledge Becomes Invisible with Experience. Tacit knowledge, or the things we know without being able to say what they are, plays a central role in mastery. But that same receding of conscious awareness can make it tricky to learn from experts who forget what it’s like to be a beginner.Difficulty Has a Sweet Spot. Fine-tuning the difficulty is central to making progress. Too hard and we fail to grasp the skill. Too easy and we may not internalize the lessons. I’ll show how you can tweak the difficulty to maximize growth.The Mind is Not a Muscle. Understanding proceeds from having the right metaphor. Unfortunately, for many of us, we have the wrong metaphor for the mind and the wrong idea about what improves through practice.Variability Over Repetition. Following the training of jazz musicians, I’ll show the surprising research on variable practice, an underused strategy for making your practice more efficient.Quality Comes from Quantity. Creative success is, to a surprising degree, the direct outcome of productivity. I’ll explain the difference between routine and creative expertise, and how you can have more creative hits in your career.Experience Doesn’t Reliably Ensure Expertise. Decades of experience doesn’t lead to great predictive abilities in many professions. I’ll share when you should trust your gut and stick to the numbers, as well as how you can adopt strategies used by professional poker players to enhance your feedback.Improvement is Not a Straight Line. Progress is not a steady ascent—there are dips and detours along the way. I’ll talk about how you can continue to reach new heights, without getting stuck in the valleys below.Practice Must Meet Reality. There’s a lot about life we can’t learn in a classroom. But learning in the field has its own dangers that must be avoided. Following the worst aviation accident in history, I’ll share the history of pilot training—and the implications for mastering your profession in the real world.Fears Fade with Exposure. Despite substantial empirical evidence in its favor, exposure remains an underused strategy for dealing with fears and anxieties. I’ll show how one of the biggest failed predictions in psychology’s history can teach us lessons about dealing with the more mundane trepidations we face in our own quest to improve.

Along the way, the book delves into the fascinating and diverse science of learning, including case studies such as:

Why it took two decades for Tetris players to begin to master the seemingly simple game.Why professional poker players make better predictions than psychiatrists.The secret to the artistic training of Renaissance master painters.How jazz musicians learn to improvise.A mathematical problem that took three centuries to solve—and the psychological theory that explains its resolution.What the London Blitz teaches us about the neuroscience of anxiety.

Whether you’re unsure where to begin, feel stuck after years of effort, or simply want a deeper understanding of how learning works, Get Better at Anything will guide you through your learning journey.

Click here to find out more about the book.

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My New Book Comes Out Next Week!

My new book, Get Better at Anything, comes out next week! 

This is a friendly reminder that the $400 worth of preorder bonuses, including my full course Make it Happen!, are only available for those who order the book before it is released on May 7th, 2024.

You can buy any version of the book (hardcover, paperback, Kindle, Audible, etc.) from any platform in any country to be eligible for the bonuses:

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If you’ve already preordered, but haven’t submitted your receipt for the bonuses—you can do so here. Feel free to email our customer service team if you get stuck: support@scotthyoung.zendesk.com.

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My 7 Rules for Happiness

Happiness is a paradoxical goal. We all want to be happy, yet we often fail spectacularly at predicting what will make us happy.

We pursue goals like wealth, fitness, status and mastery only to find that achieving them doesn’t really change our lives very much. In contrast, we often deliberately make ourselves miserable: we worry about things we cannot control, hold grudges against people we cannot influence, and spend time on activities we don’t truly value.

I don’t claim to have discovered the recipe for perpetual bliss, but from my decidedly unenlightened perspective, I have found a few maxims that have made my life better that I’d like to share:

1. Embrace the seasons of your life.

Unhappiness is wishing you could be at the beach when it is snowing. You can’t control the weather, and daydreaming about a possibility that isn’t practical doesn’t make you better off.

A major philosophical tension in the pursuit of happiness is the conflict between accepting things as they are and striving to change them for the better. There is a third way: accepting the broadly unchangeable factors of your life while seeking to make the most of the things under your control.

Weather is an apt analogy for this. Every phase of life is a season that affords some possibilities and constrains others. Happiness is largely about maximizing the opportunities afforded by your current stage of life—and not despairing of the constraints.

For instance, when I was in university, I sought to maximize my experience: I went on exchange, joined student council, went to parties and tried out new activities. Now as a father of two, I’m content going to farmer’s markets and making sand castles at the beach. 

2. Striving is good, but achieving is overrated.

Most of the time, achieving goals won’t make you any happier than you are right now.1

While some believe this fact about human nature implies striving is wasteful, I think it’s more accurate to say that while achieving goals is overrated, striving toward them is underrated. 

Goals, projects, interests and pursuits absorb our attention in positive directions. They take us away from abstract worrying or depressive navel-gazing. Activity is energizing, which is one reason why a crucial part of treatment for depression is simply getting patients to do more things.

The secret of the pursuit of happiness is that happiness is in the pursuit.

3. Meet other people more than halfway.

We are a chronically self-obsessed species. Nearly all of our thoughts are directed towards ourselves. Even our outwardly directed thoughts are often self-centered: We care about our relationship with other people. Absent our interest in them, we direct vanishingly little mental capacity towards others’ viewpoints.

This observation may seem cynical, but I’d argue it’s quite useful: if you believe everyone is self-obsessed, it implies you shouldn’t overweigh how much time others spend thinking about you. 

Relationships can break down because our slights to others are often invisible to us. In contrast, we feel the sting of every missed birthday wish, dropped calendar appointment or subtle criticism. 

If you accept this asymmetry, it makes sense to strive to meet others more than halfway. Be the one who reaches out to find time to meet. Be the one who congratulates and remembers important events. Be the one who is thoughtful and kind. When you aim to meet people more than halfway, you’re much more likely to connect in the middle.

4. Apologize often.

Everyone knows friends or family members who won’t speak to one another because of long-held grudges. While obligatory distance sometimes makes sense—especially in cases of abuse—many of these grudges began with some superficial slight that worsened over time.

Just as meeting people more than halfway can overcompensate for our built-in ego-centrism, often, apologizing can smooth over disputes and prevent feuds from festering.

This isn’t just an emotional plea, either. Game theory bears this idea out. The Prisoner’s Dilemma is a classic game:  you can rat out your partner to get the best deal for yourself, or cooperate and endure a mild punishment. If you only play once, the best strategy is to be selfish.

However, relationships are, by definition, a series of repeated interactions. In those scenarios, the best strategy is called tit-for-tat with forgiveness. This approach isn’t as soft as being a total pushover, but it also prevents continued cycles of reprisal simply because one person accidentally made a mistake. 

5. Stop listening to people who are paid to make you angry.

Our brains weren’t designed for social media. They developed in an era when dangers and norm violations needed to be quickly spotted and dealt with. This makes threatening, rage-enabling and anxiety-inducing news so particularly appealing.

Except we now live in a world with billions of people. Statistically speaking, something terrifying, enraging and panic-inducing is happening to someone at every moment. In past eras, we were mostly able to ignore such things because limits in reportage and norms of journalistic practice prevented events that happened far away or were of limited newsworthiness from entering our field of consciousness.

However, algorithms designed to maximize engagement now funnel every enraging triviality to the front of your attentional space.

We need to curate our online consumption so that we’re not unwittingly making ourselves miserable over the statistical certainty that someone, somewhere, is doing something awful.

6. Look for small novelties.

When I was in university, a guy named Nick lived on the same floor in my dormitory. Every week, a shuttle would pick up the students to go to a nearby supermarket (it was a fair walk, few of us had cars, and this was Winnipeg, where winters can reach -40 degrees Celsius).

Nick had a habit of buying one new food item every time he shopped, usually from the international section. Buying a bag of Mexican candy or an odd-looking fruit you had never heard of before might seem trivial. It certainly does not require great effort, cost or ability, but he got to experience something new each week. 

I didn’t stick with Nick’s habit consistently, but I find the same small bit of joy every time I try a cuisine I’ve never eaten, explore an unfamiliar park or stroll down a street I’ve never walked down before.

We spend years earnestly striving toward things we think will bring us happiness, but the truth is that much of happiness lies in little joys and moments that we can easily overlook if we’re not paying attention.

7. Remember everything is a choice.

Within every constraint is a choice. Every forced option contains a range of possibilities. Behind everything that must be done is a decision about how to think about it.2

In 2018, I went on a ten-day meditation retreat. The biggest lesson I took from that experience was that even within the most confining situations—such as the necessity of sitting in a rigid position for hours while only thinking about your breath—there is a world of choice available.

Psychologists have long known that our locus of control for an event greatly impacts our perception of it. Awful things feel much worse when they’re uncontrollable. In contrast, believing that we have control makes even horrible events bearable.

There will always be limits on what we can choose, but there will also always be space within those limits to make a choice. Reminding ourselves of that is often enough to regain the feeling of control, and reduce the feeling of helplessness in facing the things we cannot.

Those are my rules for happiness, what are yours? What things do you try to live by that make you happier? Share your thoughts in the comments.

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When Procrastination is Productive

I recently had an email exchange with a reader who, in his words, claimed to struggle with a lack of commitment. 

He wanted to study machine learning, but he couldn’t get past the first few modules of the course he’s taking. After a few emails back and forth, however, I discovered the reason he “wasn’t able to commit” was simply that his full-time job and family responsibilities kept getting in the way.

I don’t think this reader’s problem was a lack of commitment. 

When genuinely more important things interfere with side projects, procrastinating on the side project is the correct thing to do! His problem isn’t a lack of commitment; rather, it’s a failure of logistics, difficulty organizing whatever leftover time was available to make some progress on his goal. 

There’s a myth in self-improvement circles that everyone ought to be able to achieve any goal. Any failure to do so is seen as a lack of willpower, motivation or “commitment.” Not only is this profoundly untrue, but the mindset it generates leads to an impasse that makes it impossible to make real progress.

Maintenance and Aspirations

The best way to achieve a goal is to expend overwhelming effort working on the task. Do 10x as much as you think you need to. For ambitious goals in which few people typically succeed, this may be the only thing that raises the probability of success.

But not all goals are like this. Many are like the reader’s desire to study machine learning: It’s not overwhelmingly difficult, but it is time-consuming and probably falls in the bottom ten of his overall life priorities. 

One solution to the problem is to accept the priority is low and give up any expectations for achieving it. Sometimes, this is wise. I have dozens of learning projects I don’t work on at all simply because they’d take too much time from things I consider more important. Procrastination on low-priority items is not a vice.

But my reader’s situation was a little different. The project he had in mind was his big aspirational goal—that was being preempted by all the mundane things he had to do to keep his life from falling apart.

In my course, Make it Happen!1, I discuss an important distinction between maintenance activities and aspirational goals:

Maintenance activities are things you need to do to keep your life functioning. Washing dishes, doing your job, spending time with friends and family, paying your taxes, etc. You need to wash dishes, but there’s no aim to be the world’s best dishwasher or achieve new levels of cutlery cleanliness.Aspirational goals are those things you want to improve or achieve that transcend the status quo. Staying relatively in shape is a maintenance activity. Running a marathon is an aspirational goal.

Achieving goals is an optimization problem. Maintenance activities form the constraints on your life, and you try to optimize progress on the aspirational goals with what’s left.

Of course, this distinction isn’t rigid. Keeping a good relationship with my wife might be a “maintenance” activity by this definition, but I’m hardly an excellent husband if I only do the bare minimum. Similarly, you might set the goal of getting in shape (aspirational) but the mechanism for achieving it is a hard rule about jogging every day, which creates a fixed constraint on your time.

Still, distinguishing between them is useful since maintenance activities tend to be sustained by routines and habits, whereas aspirational goals, by their nature, require conscious planning, problem-solving and effort and thus tend to be more deliberate.

Making Progress on Low-Priority Projects

So what should this reader do to make progress on his aspirational studying goal (or at least stop feeling guilty about procrastinating on it)?

Taking a complete inventory of your maintenance activities is an essential first step. What things do you have to do to keep your life on track?2

Next, you need to prioritize among your outstanding aspirational goals. When you look at each goal in isolation, it’s easy to think you have time for each of them, and then chastise yourself for not pursuing them. But unless you’re actually listing them all out and deciding what matters most, that’s a recipe for guilt, not success.

Priorities don’t need to be unchanging. It’s fine to prioritize programming this month and then next month focus on getting in shape. The problem is that when you try to prioritize both concurrently, you will likely achieve neither, but you may beat yourself up about it.

Once you’ve done this exercise, you’ll have a much better picture of what you can reasonably accomplish. If you realize you only have thirty minutes a day after all your maintenance activities, you should scale your ambitions to that fact. Thirty minutes a day might be enough time to pick up a new hobby, but it’s probably not enough time to launch a start-up or self-study a degree in a typical timeframe. 

Being Okay with Procrastinating on Low-Priority Goals

Having a lot of maintenance activities is normal. It means there are things in your life that are valuable to you that you don’t want to lose. It’s perverse to argue that a person who can’t launch a start-up because they have a career and a family somehow exhibits a defect in commitment. 

Often, maintenance activities tend to grow as you become more successful. While there are some cases where success leads to shedding responsibilities, the more typical case is the opposite. Nobel-prize winners are lauded, but they also tend to have less time for original research as the acclaim comes with press attention, honorary appointments and countless requests for their time.

While a person willing to sacrifice many of the values sustained by their maintenance activities will have more capacity left for aspirational goals, that’s an issue of prioritization, not laziness. Thomas Edison was the most prolific inventor in history. He was also an inattentive husband and father. Choosing differently than he did isn’t a character flaw.

Productivity isn’t throwing out all your existing commitments in the fanatical pursuit of a big ambition. Instead, it’s choosing what matters and organizing your time and energy to reach it. Don’t let yourself feel guilty if those choices cause you to procrastinate on something that genuinely matters less.

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The Science of Mental Models

Human reason is a puzzling ability. As a species, we’ve invented logic, mathematics, science and philosophy. Yet we suffer from a list of cognitive biases so long that an entire Wikipedia page is devoted to categorizing them.

So which is it: are we excellent at reasoning or incurably irrational?

Psychologist Philip Johnson-Laird has spent his career working out the answer. His theory of mental models explains how we have the ability to reason correctly—and also why we frequently fail to do so.

I recently read Johnson-Laird’s nearly 600-page book, How We Reason. The book weighs in on an impressive variety of topics related to reasoning:

Why do some people reason better than others? (Mental models require working memory, which is both limited and varies between individuals.)Why are some puzzles harder than others? (The more mental models a correct inference requires, the harder it is to deduce the correct answer.)Does reasoning differ between cultures? (Johnson-Laird argues the basic mechanisms are universal, but there can be differences in knowledge or strategies.)Do people with psychopathologies reason more poorly? (It may actually be the opposite! People with obsessive-compulsive disorder actually reason better when the content of the reasoning questions was related to their obsessions.)How does visualization impact reasoning? (Images often occur alongside reasoning, but imagery itself can actually make reasoning worse!)Can we improve our ability to reason? (Johnson-Laird is cautiously optimistic, suggesting a method based on mental models his research found dramatically improved performance.)

To understand these questions, and Johnson-Laird’s proposed answers, let’s walk through what the theory of mental models argues, how it differs from some prominent alternatives, and what that implies about how we think.

What are Mental Models?

To understand the theory of mental models, let’s look at a basic syllogism:

All men are mortal.Socrates is a man.Therefore … 

According to Johnson-Laird, we reason by simulating possibilities described by the premises: As we read the first premise, we generate a mental representation of a few men and assign each of them the property “mortal.” As we read the second premise, we pick out one of these anonymous men and indicate that it is Socrates.

man – mortal – Socrates
man – mortal
man – mortal
…

Inspecting this model, we can immediately conclude that Socrates is mortal.

Mental models are not the same thing as mental imagery. It isn’t necessary to visualize little Athenians in togas, one of whom is Socrates, to make the correct inference. 

Indeed, a property like “mortal” doesn’t have an immediate visual representation, so it’s not obvious how it could be inferred from an image.

Mental models are abstract, but they are structured in a way that reflects the situation they represent. 

For instance, Johnson-Laird explains that when we mentally rotate an object, the mental model we’re rotating is a three-dimensional representation of the object. However, the mental image we see in our mind’s eye is the image of that 3D object when viewed from a particular vantage point.

While imagination often feels like a movie we can play and rewind, mental models are static. When we reason about a dynamic situation, such as figuring out the effect of turning a particular gear in a complex machine, we typically simulate each component’s effect sequentially. Our reasoning is like a static diagram we draw and erase from as we work through each step.

In brief, a mental model is not an image or a movie. Rather, it’s an abstract representation that contains one possibility based on the information given. We reason by manipulating these representations: adding properties, moving them around and inferring conclusions by directly inspecting them. The contents of the mental model are conscious, but the mechanisms used to generate and represent them are not. Thus, while we can use mental models to reason, we can’t directly report how they’re organized in our minds like we can for mental imagery.

Why is Reasoning Possible? Why Does it Often Fail?

I started this book review by noting the puzzle of human reason: We’re a species that has invented calculus—yet frequently fails at basic arithmetic.

Consider the following question:

A bat and a ball cost one dollar and ten cents. The bat costs one dollar more than the ball. How much does the ball cost?

Many people instinctively respond with “ten cents,” but that can’t be right. If the ball cost ten cents, the bat would need to cost one dollar and ten cents, bringing the total to $1.20. The correct answer is five cents, but many otherwise intelligent respondents get it wrong.

Dual-process theories suggest that we use two different psychological systems to answer these questions. System 1 is fast, automatic and effortless. Because ten cents and one dollar and ten cents are both in the problem statement, and one dollar is their difference, the question immediately provokes a tempting System 1 answer to the question: ten cents.

System 2, in contrast, is slow, effortful and calculating. Mental models are a theory of how reasoning happens in this system. Rather than the rapid, intuitive response given by the first system, to use mental models, we need to mentally construct the situation described and inspect it to determine the right answer. Many failures of our reasoning are simply accepting a tempting System 1 answer instead of doing the hard work of reasoning the question out using System 2.

Reasoning failures don’t occur just because we rely on misleading intuitions. They can also happen when the problem requires us to generate more mental models than we can fit inside our limited working memory.

Some reasoning problems are relatively straightforward. Consider the following:

Some of the authors are bakers.
All of the bakers are bowlers.
What, if anything, follows?

The easy deduction, “Some of the authors are bowlers,” occurs quickly because the conclusion is evident after only constructing a single mental model:

author – baker – bowler
author – baker – bowler
author 
author
…

But other problems are more difficult. Consider:

None of the artists is a beekeeper.
All of the beekeepers are chemists.
What, if anything, follows?

Some people offer invalid conclusions such as “None of the chemists is an artist,” or state that nothing interesting follows from those premises. Very few, according to Johnson-Laird, draw the correct conclusion, “Some of the chemists are not artists.”

Why is this problem so much harder than the first?

We need to construct not just one, but three different mental models to represent all the distinct possibilities implied by the premises. Then, we must compare those mental models to find a deduction that holds true for all of them. 

Johnson-Laird argues that three mental models are at the outer range of our working memory capacity, so most people will fail at this reasoning. However, we can augment our working memory by offloading some of the models to pencil and paper. 

In an intriguing experiment, subjects were given a pencil and paper and instructed: “Try to construct all the possibilities consistent with the given information.” This approach encourages people to generate more models and makes it more likely that they can draw a correct inference.

Johnson-Laird compares the performance with reasoning puzzles:

Without the benefit of the model method, the participants were right on about two-thirds of the trials, and they took an average of twenty-four seconds to evaluate each inference. With the benefit of the method, however, they were right on ninety-five percent of the inferences, and they took an average of fifteen seconds to evaluate each inference.

Mental models are cognitively demanding. Thus, we often fail to construct a mental model and go with a cheap and fast System 1 response. Or, we get so bogged down in trying to construct alternative possibilities that we fail to make a valid conclusion. Yet our difficulties in reasoning are not insurmountable. Johnson-Laird explains:

If humans err so much, how can they be rational enough to invent logic and mathematics, and science and technology? At the heart of human rationality are some simple principles that we all recognize: a conclusion must be the case if it holds in all the possibilities compatible with the premises. It doesn’t follow from the premises if there is a counterexample to it, that is, a possibility that is consistent with the premises, but not with the conclusion. The foundation of rationality is our knowledge that a single counterexample overturns a conclusion about what must be the case. [emphasis added]

Comparing Mental Models with Alternative Theories

Mental models are a neat theory, but is the theory true? Proponents of a given theory can nearly always point to compelling evidence in its favor. Sometimes, even spurious theories can sound plausible, especially if you haven’t devoted a career to noticing their flaws.

So, what’s the status of mental models?

My understanding is that Johnson-Laird’s theory is one of the leading psychological theories of reasoning, even though it doesn’t have the status of being a consensus theory (few theories in psychology do). The principle alternatives (which Johnson-Laird spends quite a few pages debating against) are:

Formal theories. In these theories, we reason the same way that logicians do, paying attention to the logical structure of sentences rather than their meaning. Bayesian networks. Bayes’ rule is a way of updating the amount of confidence in a belief when we encounter new evidence. Some theorists argue that our brains implement a version of this rule, allowing us to make inferences with incomplete or uncertain information.Content-specific rules. Instead of a single broad reasoning faculty, perhaps we reason with different mechanisms for different types of situations. One prominent theory, for instance, explains reasoning failures as an ability to detect rule violations rather than a general ability to reason about conditionals.1Practical Takeaways of Mental Models

Inferring practical tips from a purely descriptive theory is often speculative. Research into general problem-solving heuristics encouraged many researchers to consider instruction in problem-solving skills as critical, but we now realize that was probably a mistake.

Similarly, while it’s easy to squint at Johnson-Laird’s results and come up with takeaways, some of those are probably illusory. His finding that imagery may interfere with mental models shouldn’t imply that suppressing mental imagery is necessarily helpful. (Indeed, a lot of anecdotal evidence in math and physics suggests the opposite!)

With that caution in mind, a few tentative takeaways of mental models might include:

Use pencil and paper to construct complete models for complex situations. Programmers, for instance, often introduce bugs when they fail to mentally simulate all the possible settings for variables and an unanticipated combination of settings results in an error. Working through all the possibilities with pencil and paper can help to overcome insufficient mental models.Knowledge supports reasoning. According to mental model theory, knowledge modulates our interpretation of logical premises. A complete logical model of “Jim’s either in Rio or he’s in Brazil” includes the possibility that he’s in Rio but not Brazil. However, most participants never consider this possibility because they know that Rio is in Brazil. Knowledge trims extraneous possibilities and allows for reasoning to proceed with less cost to working memory.Use base rates to avoid thinking mental models have the same probability of being correct. Johnson-Laird argues that we reason about probabilistic events by constructing possibilities and weighting them equally by default. But this biases our reasoning toward rare and unusual events. Plane crashes are easy to visualize, but occur very rarely, so we overweigh their likelihood compared to the thousands of interchangeable mental models when the plane lands without incident. Base rates, the practice of assigning probabilities based on the statistical likelihood of similar events, can improve the accuracy of risk calculations.Discuss and share your mental models. A weak point of human reasoning is that we struggle more with generating counterexamples than we do with recognizing them. The famous Wason four-card task tricks most participants, but accuracy improves greatly when people are allowed to discuss and explain their choices, as there is an increased chance of successfully recognizing a counterexample.

If you’re interested in learning more about mental models and Johnson-Laird’s theory, I can recommend both his original 1983 book, Mental Models, as well as his 2006 summary of the state of the theory, How We Reason.

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How to Learn Vocabulary in Another Language

When it comes to language learning, grammar gets all the attention. Most theories about second language acquisition focus primarily on how we acquire syntax—the way we put words together to form sentences.

Vocabulary, in contrast, has historically been outside of the spotlight. This is unfortunate. Research has shown that your vocabulary has the greatest effect on comprehension.1 A major difficulty students have in learning a new language is the sheer volume of new words.

Given its importance, I was pleased to encounter Stuart Webb and Paul Nation’s How Vocabulary is Learned, a research-based handbook for teachers that looks at how to deal with one of the central difficulties in becoming fluent.

How Many Words Do You Need to Know?

The first step in any journey is figuring out how far you are from your destination. How many words do you need to learn to be fluent?

Right away, this question runs into difficulty. How should we count words?

One way would be to count every distinct spelling in a set of texts. Except, this approach drastically overcounts. It would include regional spelling differences (e.g., color in the US, colour in the UK) as well as numerous grammatical inflections that are almost certainly not stored in the brain as separate words (e.g., difficult, difficulty, difficulties, etc.).

Instead, researchers usually prefer to count word families—which include not only distinct words, but also their related inflections, variations in spelling and derivations. 

How many word families do you need to know?

Here, we can take advantage of an empirical finding known as Zipf’s law. This law states that if we rank every word in a language in order from most-used (rank = 1) to next-most used (rank = 2) to least-used (rank = n), a word’s frequency of use is proportional to the inverse of its rank. 

This means that the frequency of the second most common word would be roughly equal to 1/2 times some constant. In contrast, the frequency of encountering the 10,000th most common word would be roughly equal to 1/10,000 times that same constant. We would expect to see the second most common word 5000 times for each time we see the 10,000th one.

The first 1000 word families in English, for instance, account for roughly 85% of the words encountered (e.g., baby, cake, dad), and the next 1000 account for only 4.3% (e.g., background, accent). By the time you get to the 20th batch of 1000 words, they make up less than one-hundredth of one percent of the available words (e.g., abaya, chiastic).

Knowing just the first 2000 word families would cover roughly 80-90% of most novels, newspapers, conversations, television shows and academic lectures.

The upside of this finding is that you can quickly get to the point where you know most of the words being used.

The downside is that low-frequency words are often essential for understanding what is being said. Native English speakers tend to know between 15,000 and 20,000 word families. Since most of those words occur increasingly infrequently, the amount of exposure needed to learn all of them properly can be staggering.

What, then, should a practically-minded language learner focus on? Webb and Nation suggest two goals might be important:

Knowing 3000 word families “would allow learners to understand 98% of the words in most graded reading materials, as well as 95% of the vocabulary used in spoken discourse.” Knowing 95% of the words used is often considered the threshold for understanding, so learning the 3000 most common word families is a good beginner goal.Knowing the 9000 most frequently used word families, which include both low- and mid-frequency words, “equates to sufficient vocabulary knowledge to understand speech and writing,” Webb and Nation continue, arguing that “differentiating between the words before and after this point is useful.”How Should You Learn Words? The Power of Extensive Exposure

When it comes to our native languages, researchers already have a pretty good idea of how we learn words, and it’s not through studying vocabulary lists.

In a celebrated series of papers, William Nagy, Richard Anderson and Patricia Herman found that school children add as many as ten new words to their vocabulary per day for several years.2

Maintaining such a pace with explicit teaching would be next to impossible, and yet native speakers not only learn this many words—they integrate them seamlessly into their actual language use.

Obviously, the process of learning words in one’s native language is not like memorizing definitions from a dictionary. It’s not as if native speakers are being assigned ten words every day to memorize. Instead, their extensive exposure to words in their environment lets them steadily accrete knowledge of new word meanings so that, averaged over long periods, they learn roughly ten new words per day.

This alone shows that long-term exposure to an immersive linguistic environment is enough for vocabulary growth.

However, native speakers also accumulate tens of thousands of hours of meaningful language input and use—far more than most people can achieve studying a foreign language!3

Should You Use Flashcards to Learn Vocabulary?

Extensive exposure is sufficient for vocabulary learning. Yet, given the huge quantity of speaking, reading, and listening time one gets with a native language, exposure alone may not be enough for the typical language student.

Flashcards offer a quick and effective way to acquire a large vocabulary, especially when the meanings of words can be readily paired with their counterparts in one’s native language. Spaced repetition systems, like Anki, also offer the opportunity to store large libraries of cards and allow reviews to be queued so that studied words won’t be forgotten.

However, flashcards for single words also have disadvantages. Learning a word in isolation can cause you to miss its connotations or usage in particular contexts. Cards that focus on a word family may also omit some of the possible grammatical modifications of the word and their meanings.

Cautioning on the exclusive reliance on flashcards, Webb and Nation write:

“It is also important to remember that learning with flashcards is only one step in the vocabulary learning process. … Flashcards should be supplemented with plenty of meaning-focused input that includes the target words to provide models for how these words are used, and meaning-focused output that provides opportunities to use them.”

Other Advice for Learning Words

Webb and Nation review some other advice on learning vocabulary:

Words with similar meanings should be taught on separate days. Teachers typically cluster words by categories (e.g., days of the week, colors, occupations, etc.), but research indicates this may be unwise. Students show greater interference when items with similar meanings are taught in the same sessions.Graded readers can accelerate implicit learning. To successfully guess the meaning of a new word from its context, we need to understand between 95% and 98% of the surrounding words. This is difficult to achieve with native-level materials. Graded readers, which deliberately limit vocabulary, can be beneficial here.The rate of vocabulary acquisition depends on student enthusiasm and aptitude. Webb and Nation suggest that a dedicated student might acquire 400 word families per year, assuming they are exposed to the words in various contexts. For narrower word learning, such as from flashcards, more words can potentially be covered in a similar time frame.Learners should spend time on all four pillars of language learning:Meaning-focused input. This includes conversations, books, films, television and other media that you attend to primarily for their meaning. Input provides the raw data for learning new words and enriches the contextual associations of words studied deliberately elsewhere.Meaning-focused output. Speaking and writing are more difficult than simply understanding  particular words from input, as using words correctly requires a more precise knowledge of each word and its meaning.Language-focused learning. This is the deliberate act of memorizing words, studying flashcards, or receiving explanations about word meanings. Webb and Nation argue that this should account for ~25% of the time spent learning a language.Fluency development. Finally, attention should be paid to activities that speed up the understanding and production of words already known. Familiar materials that enable quick reading or conversations on familiar topics may not be needed to build new vocabulary, but they reinforce what was learned previously.

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5 Keys to Get the Most out of the Feynman Technique

The most popular piece of studying advice I’ve ever come up with was the Feynman Technique. While the technique itself is only loosely based on Richard Feynman’s practices, the idea of self-explanations took off and became a studying meme that has since gone far beyond my own audience.

The basic idea for the technique is simple:

Pick an idea you don’t understand very well.On a blank sheet of paper, write an explanation for the idea as if you are teaching it to someone else.Whenever you get stuck, go back to the textbook to fill in the gaps in your understanding.

This technique was a staple of my studying during my MIT Challenge, and I still have dozens of pages I wrote while trying to work through tricky concepts in math, physics and programming.

That being said, there’s a lot of nuance in getting the technique right. Here are five tips to make self-explanations work for you:

1. Apply the technique selectively.

The biggest complaint about the Feynman technique is that it takes too much time. A course may cover a dozen or more concepts in a single lecture. Writing a couple pages of explanation for each is incredibly time consuming. 

Worse, the time spent building your abstract understanding of a concept is time not spent learning to apply it. If you’re cutting out practice in favor of self-explanations, you may end up worse off!

Therefore, the first piece of advice I have is to apply it selectively. I tend to rely on self-explanation when:

I cannot successfully apply a concept to the assigned practice questions,The idea doesn’t make sense to me,AND I know the idea is central to understanding the class.

My usual approach is to start with reviewing all the course material (lectures and texts) and then to do relevant practice problems. Only when I hit an impasse that I believe is owing to a conceptual misunderstanding does it make sense to invest the time to do a full self-explanation.

2. Go tight for debugging confusion.

One reason to apply self-explanations is to repair a misunderstanding or fill in a gap in your knowledge that’s causing confusion.

Unfortunately, figuring out precisely what you don’t understand is often half the battle. Most of the time, we don’t have a good sense of what is confusing us; we’re simply confused.

The value of self-explanation is that by trying to generate the explanation yourself, in your own words, you’ll invariably hit an impasse at the weak link in your understanding. Knowing where that impasse is, which link is weak, gives you the tools to go back to your notes or textbooks, to do a close reading, or to articulate good questions you can ask teachers or peers.

However, this only works if your explanation is tight enough. If you explain the concept to yourself in broad terms, you might skip over the missing link in your reasoning. You can easily convince yourself you understand something without identifying or repairing the missing step in your reasoning.

Thus, if you’re doing a self-explanation because of a struggle with a particular problem—start by explaining that exact problem. If it was a confusing section of a lecture—explain the confusing section of the lecture. Stepping back and tackling a generic concept can skip over the very details you need to understand.

3. Focus on big ideas for memorability.

Another use for self-explanations is when you basically understand an idea, but the reasoning feels difficult or a bit fuzzy. It feels like you don’t automatically “get” the idea, and like you’ll probably forget it later.

One way to think about this is to imagine that the explanation consists of a bunch of different individual facts, steps in reasoning, and mental simulations. Confusing explanations are those where the linkages between various parts haven’t quite solidified to the point where you can mentally traverse the idea with ease. By trying to generate the explanation yourself, you reinforce the linkages between the factual propositions and the steps of reasoning that make up the understanding. 

Once you have the general gist of an understanding, generating analogies, diagrams or examples can be helpful. These further associate the ideas in the explanation with different, overlapping explanations, making them easier to retrieve and walk through in the future.

4. Don’t substitute self-explanations for genuine practice problems.

Self-explanations can help you focus your attention, debug a particular missing link in your understanding, or elaborate and retrieve knowledge to generate an understanding of a higher-level concept.

However, it’s important not to substitute self-explanations for doing genuine practice. There are two reasons for this:

– First, it’s hard to get the level of depth right for explanations. For hard classes, the tendency is to do self-explanations that don’t go deep enough, thus skipping over misunderstandings (even with deliberate effort to avoid this). Doing practice problems helps you calibrate how deeply you need to understand an idea.

– Second, learning to identify problem types and common solution steps or approaches relies on different knowledge than generating an overall explanation does. Since the knowledge needed to, say, explain Kirchhoff’s laws only partially overlaps with the procedural knowledge needed to solve for the output of a circuit diagram, transfer from one to the other will only be partial. Therefore, if the aim is to pass an exam, omitting practice questions that resemble those on the test is unwise.

Self-explanations primarily help when:

You hit an impasse with your practice, owing to a confusion or misunderstanding.Practice problems are limited or overly narrow. If the problems don’t cover the full space of potential applications for an idea, developing a deeper representation of the underlying concepts may be helpful.5. Seek out alternative explanations to fill missing pieces.

Often, close reading and careful reasoning can fill gaps in misunderstanding without resorting to new materials. Usually, the issue is simply that you missed an important step in reasoning in the first pass, resulting in confusion.

However, this isn’t always the case. Sometimes a teacher assumes a crucial fact or step is obvious and omits it from their explanation, even though it may be necessary for making the correct inference. In other cases, an idea is alluded to, but if you haven’t mastered that idea, the explanation as a whole doesn’t make sense to you.

Knowing where exactly the gaps are in your understanding makes it easy to seek new explanations. Some good strategies include:

If the concept skipped over was a prerequisite you haven’t mastered, finding a Khan Academy or online video that explains that concept may help.If the reasoning between two steps isn’t clear, try finding another explanation of the same concept. Different teachers will skip over different facts, so coverage from multiple explanations is better than one.

While I haven’t used it as extensively myself, tools like ChatGPT also seem powerful in this regard. Because you can copy and paste the text of what you’re trying to understand and request a summary or explanation, LLMs may be able to offer useful explanations of steps you missed. Of course, as with all AI-based software, there will sometimes be hallucinations—but it’s generally easier to verify whether a single missing step of reasoning fits.

Teach to Learn

Although I don’t take nearly as many formal classes as I used to as a student, self-explanations are still the bulk of my work—writing for a living requires me to make sense of newly learned information so I can include it in my essays. As the saying goes, “Those who know, do. Those who understand, teach.”

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My New Book: Get Better at Anything (Special Offer for Preorders)

My new book, Get Better at Anything: 12 Maxims for Mastery, is now available for preorder.

This book means a lot to me. It’s easily the longest and hardest I’ve ever worked on any project in my life. From start to finish, the project took over four years, involving hundreds of books, several hundred scientific papers and countless conversations with leading experts to assemble.

The book digs deep into the fascinating science of learning. I cover the fundamental principles researchers have uncovered that explain how learning works, condensed into twelve memorable maxims you can use to guide your own efforts at improvement. 

Along the way, I tackle some fascinating case studies, such as:

Why did it take two decades before Tetris players began to master the game?Why professional poker players make better predictions than psychiatrists.What was the secret to the artistic training of master painters during the Renaissance?How do jazz musicians learn to improvise?A mathematical problem that took three centuries to solve, and the psychological theory that explains its resolution.What the London Blitz teach us about the neuroscience of anxiety.

If you read my previous book, Ultralearning, Get Better at Anything is the perfect sequel. I’ll dive deeper into many of the important issues raised by the first book, while bringing up new stories, science and practical advice.

Even if you haven’t read Ultralearning, you can start reading Get Better at Anything without any prior preparation. I wrote it so that the person who may only read one book in their life on this topic would leave with a comprehensive and useful picture of the science of learning.

Why You Should Preorder Get Better at Anything. (Don’t Wait Until It’s Released.)

I’m proud of this book, and I want it to do well. Unfortunately, authors face somewhat perverse incentives when marketing a book like this one.

While we’d all like a book to stand and fall only on its merits, that isn’t really how publishing works. The vast majority of published books are invisible. Even large bookstores stock only a tiny fraction of what is published annually.

Instead, the books that gain an initial burst of readers are much more likely to get media attention, be stocked in bookstores and be recommended on platforms like Amazon or Audible. Fail to reach a critical threshold of attention, and even great books languish in obscurity.

Preorders, in this system, are very important. They signal to bookstores that the book should be stocked, they tell Amazon to use it for recommendations, and are a major factor in determining whether a book winds up on bestseller lists.

If you’ve been enjoying my writing, and were thinking about reading my next book, I have a small request: preorder the book instead of waiting until it comes out.

My Giveaway for Readers that Preorder (Get $400+ of My Stuff for Free)

To sweeten the deal, I want to make a special offer to anyone who preorders the book.

If you preorder the book in any version (hardcover, Kindle, audio, etc.) and send in your receipt, I’ll give you:

My full course, Make it Happen! (normally sold for $297). This six-week course covers the psychology of motivation, goal-setting and behavior change—the perfect resource for making the goals you have happen in your life.All four of my previous, self-published ebooks (normally sold for $114). This includes: Learn More, Study Less . My first book on learning. Has sold over 500,000 copies, worldwide. The Little Book of Productivity . Ninety-nine ideas to make you more productive and less stressed at work. Think Outside the Cubicle . A revised way of thinking about productivity for students, freelancers and entrepreneurs. How to Change a Habit . My popular guide to changing your behavior, and your life.A private, one-hour live, fireside chat. For those who preorder, I’ll have a live call (recorded, for those who cannot attend), summarizing some of the main lessons from the book, and sharing some stuff that I couldn’t fit in.

To help me out with this new book, and to get access to all of the giveaways, all you need to do is preorder the book from any platform in any version, then submit your receipt here. The book will not be available until May 7th, but the preorder bonuses will unlock as soon as we can verify your purchase.

Links to pre-order:AMAZONAPPLE BOOKSBARNES & NOBLEBOOKS-A-MILLIONBOOKSHOPGOOGLE PLAYHARPERCOLLINSKOBOINDIGOAUDIBLE

Side note: While the exact version of the book doesn’t matter to get the bonuses, if you do choose to get a hardcover version of the book, that is somewhat more helpful for me (many bestseller lists discount digital copies). However, I know that getting hardcover copies can be difficult for people overseas, so if you choose to preorder on Kindle—that’s perfectly fine too!

Since this offer is only for preorders, I should stress that the book needs to be preordered on or before May 6th, 2024 to qualify for the giveaways.

How To Get Preorder Bonuses

Once you’ve preordered the book, simply go to this page and follow the instructions to upload your purchase receipt.

_ _ _

I sincerely hope you enjoy the book. Readers like you are the reason I’m able to do this for a living, and for that I’m eternally grateful.

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The Infinite Library Problem

Studying for school can be hard, but the process is pretty straightforward:

Read all the material and attend all the lectures.Practice in ways that mirror what will be on the final exam.Use feedback to figure out what you need to fix.

Outside of school, though, this strategy breaks down. It’s impossible even to be aware of all the books that might be useful for any given subject, never mind actually reading them all. 

In an infinite library, choosing what material to learn dwarfs even the largest difference in studying efficiency.

How to Choose What to Learn Next

Learning in an infinite library is a classic explore-exploit problem. You can choose to browse the library, read a few books, or study one intensively. Time spent on any option means less remains for the others.

Unfortunately, explore-exploit problems are intractable. There’s no method that will guarantee you’re spending your limited learning time wisely.

While there’s no surefire method to optimize the decision about what to learn next, I’ve found a few helpful heuristics.

Heuristic #1: Start with Textbooks

The best book to read first on a new topic is usually a textbook. They’re written for students, so jargon is defined, and necessary concepts are explained. They also tend to be balanced in their coverage of the field. Even authors who write polemical treatises tend to give a fair report of the domain when writing a textbook.

Textbooks also serve three additional functions:

They introduce you to a field’s basic language and concepts, providing the foundation to understand more specialized books and texts.They cite key papers and research, giving you a further reading list.They introduce terms and technical language, which you can use to search for more reading material.

Literature review papers can also be useful as a first introduction to a topic. Literature reviews usually cover topics that have a relatively large body of accumulated research but are too narrow or new to fill a textbook. These can be harder to read than textbooks because they’re often written for academics and presume the reader is familiar with the discipline, if not the subtopic being reviewed.

Heuristic #2: Use Specific Projects to Overcome the Effort Hurdle

Often, reading about a topic is enough to learn what you need. Given that the alternative is often doomscrolling social media or binge-watching Netflix, simply spending enough total time learning is a vital first step.

But reading alone isn’t enough to master a topic. An almost gravitational pull urges us to avoid the more effortful parts of learning. It’s easier to watch lectures than to do practice problems. It’s easier to read a fun explainer than to dig into the primary sources. Without any forcing mechanism, self-directed learning tends to stick to the surface.

The best way I’ve found to overcome this problem is to establish specific learning projects with clearly defined goals. This is especially successful when a topic has some drudgery that must be pushed through before the skill or topic becomes naturally engaging.

Heuristic #3: Follow Smart People, Save Their Recommendations

While collecting books on your bookshelf is easier than actually reading them, the two are not in opposition. I would argue that a major reason people don’t read is that they don’t have, at hand, any books they find particularly interesting or compelling to read.

This problem is trivial to solve in today’s world. Follow people who have some overlap with your interests who regularly write book reviews or post their reading lists. Whenever a book recommendation piques your interest, download a Kindle sample to your phone. 

Then, whenever you don’t have a book you’re excited to pick up and read, scroll through and read some of your samples until one grabs your attention. While you will likely end up deleting many of the samples without reading the whole book, having them available ensures you always have a steady supply of interesting books.

Heuristic #4: Allocate 10% of Your Study Time to Weird, Unfamiliar Topics

There’s nothing wrong with cultivating particular interests and areas of expertise. But I think two factors make specialization self-reinforcing, even if we’d prefer more breadth:

Familiarity with a subject makes it easier to read new books on that subject, thus lowering the effort cost.Books refer to other books and resources in that same area, populating our future reading lists with more on the topic we are already studying.

One potential counter to this is to deliberately reading and studying books on topics you know nothing about. Some of my favorite books I’ve found through this process include books about fungus, jazz, and epistemology in ancient China.

And your studies shouldn’t just be confined to books! Bookishness is itself a kind of comfort-induced specialization that can be broadened by learning to cook or garden or immersing yourself in another culture.

What strategies do you use to decide what to read or learn next? Share your thoughts in the comments!

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The Paradox of Productivity

What if being productive doesn’t mean feeling productive?

A paradox of productivity is that the things that feel productive—working incessantly, checking off lots of tasks, feeling strained and drained—are often not what produces important accomplishments; in fact, these things can get in the way. 

Recently, two of my friends have written books which argue versions of this thesis. The first is Ali Abdaal’s Feel Good Productivity. Ali’s central idea is that we’re at our most productive when we’re happy. In the moment, grinding ourselves into exhaustion may seem like a necessary trade-off. We might be miserable, but it’s a crucial sacrifice on the altar of our ambitions. Ali argues this is a delusion. Burning ourselves out doesn’t just make us unhappy—it also makes us get less done.

The second book, Slow Productivity, written by my longtime friend and collaborator, Cal Newport, comes out this week. (I was lucky enough to get an advance copy.)

Slow Productivity builds on the ideas Cal developed in Deep Work, Digital Minimalism and A World Without Email. Cal argues that we aren’t good at measuring the productivity of knowledge workers. As a result, we tend use how visibly busy workers are as a proxy for their productivity. This pseudo-productivity makes workers feel harried, but all this busyness doesn’t lead to doing excellent work.

Drawing on dozens of well-researched case studies, Slow Productivity makes the argument for a different understanding of productivity. It is less centered on checking off items on a to-do list, and more focused on doing work that will build a legacy.

Cal proposes three principles to make this work:

1. Do fewer things.

2. Work at a natural pace.

3. Obsess over quality.

Feeling Productive Versus Being Productive

I admit, I was a little nervous to begin reading Slow Productivity. Cal and I have worked together for years, and we rarely have significant divergences in our viewpoints.

However, in my upcoming book, Get Better at Anything, I dedicate an entire chapter to reviewing psychologist Dean Simonton’s work on creativity, which finds a surprisingly tight relationship between creative quantity and quality. On the face of it, the idea that we should do fewer things and obsess over quality seems to contradict the idea that those who have the most hits are the ones who took a lot of shots.

But a careful reading of Slow Productivity indicates there is less contradiction than one might think. Cal’s opening story is of the author John McPhee, spending a leisurely eight months doing in-depth research for a magazine piece, lying on a picnic table as he contemplates how it will all come together. 

Yet John McPhee also wrote twenty-nine books and won a Pulitzer Prize! He could just as easily have been an example of Simonton’s maxim that the most successful creatives are typically prolific.

Lazing under a tree contemplating your work feels unproductive. Yet, paradoxically, this loafing is the habit of someone who is incredibly productive—measured by both critical acclaim and volume of work.

While I hesitate to put myself alongside McPhee as an example, I’ve observed this paradox of productivity in my own work. The times I have felt the least “productive” in the ordinary to-do list sense of the word have driven my biggest career leaps.

When I undertook my MIT Challenge, it was an intense project that had no direct connection to my actual work. Several friends and colleagues actively dissuaded me from taking it on since the project seemed to them a complete waste of time. I did it anyway. At the time, it looked like I was taking a sabbatical to work on an eccentric, ambitious, and perhaps pointless project. 

In retrospect, it looks like a fantastic bit of self-promotion for someone who writes about efficient learning. But, during the project, relatively few people paid attention to it, and the time it required forced me to cut back to the absolute bone every activity that was paying my bills. The year I worked on the project, I earned far less than the previous year, which had been a relatively “normal” one for my business selling ebooks and courses.

Many efforts that ultimately lead to giant leaps in your creative work seem fantastically unproductive. They require you to scale back your “normal” work drastically. They cause you to lose clients and turn down contracts. For the outside world obsessed with visible busyness, it looks like you’re just wasting time. 

But true productivity, measured in terms of one’s lifetime accomplishments, works on a different rhythm than the daily to-do list.

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