Get Better at Anything

Get Better at Anything Plot Summary

Introduction

When a child watches an adult tie shoelaces, something remarkable happens. Despite never having performed this complex sequence of movements before, the child can often replicate the basic pattern after just a few observations. This extraordinary ability to learn from watching others distinguishes humans from even our closest animal relatives. While chimpanzees might outperform human toddlers on tests of spatial reasoning or causal understanding, they fall dramatically behind when it comes to learning through observation. This book explores the fascinating science behind how we acquire new skills and knowledge. We'll discover why watching experts can sometimes be more effective than struggling through problems on our own, how finding the right level of challenge accelerates learning, and why the skills we develop are often surprisingly specific rather than broadly applicable. We'll examine why varied practice builds more flexible abilities than simple repetition, how quality emerges from quantity in creative pursuits, and why some environments provide better feedback for learning than others. By understanding these principles, you'll gain insights that can transform how you approach learning any new skill, whether it's playing an instrument, mastering a sport, or developing professional expertise.

Chapter 1: The Power of Observation: Learning from Examples

Humans possess an extraordinary ability to learn by watching others. This capacity for observational learning is so fundamental to our species that we often take it for granted, yet it represents one of our most powerful cognitive advantages. When researchers compared toddlers with chimpanzees on various cognitive tests, they found something surprising: the young children performed no better than the apes on tests of spatial reasoning, quantitative thinking, and causal understanding. In some cases, the chimps even outperformed the children! But there was one dramatic exception—when shown how to solve a problem, the children easily learned from the demonstration while virtually none of the apes could. This reveals that imitation isn't a sign of mindlessness but rather the foundation of our unique intelligence. The power of learning through observation explains remarkable real-world phenomena. Consider how Tetris players today are dramatically better than those from thirty years ago. In the early days, players were isolated, each developing their own techniques without seeing how others played. But with the advent of YouTube and livestreaming, players could watch experts and see exactly how they achieved their high scores. Techniques like "hypertapping"—vibrating the thumb to hit direction buttons more than ten times per second—spread widely once they could be observed. This transparency transformed what was possible in the game, with modern players achieving feats that were once thought impossible. Learning from examples can be surprisingly more effective than trying to solve problems on your own. Cognitive psychologist John Sweller found that students who were given worked examples of math problems learned more efficiently than those who spent the same amount of time solving problems themselves. This seems counterintuitive—surely figuring something out for yourself leads to deeper understanding? But Sweller discovered that problem-solving imposes a heavy cognitive load on beginners. When you're trying to solve a problem using means-ends analysis (constantly comparing where you are to where you want to be), you have less mental capacity left to notice patterns that might help you solve similar problems in the future. This insight helps explain why traditional apprenticeship models were so effective. Renaissance artists like Leonardo da Vinci began their training by copying masterworks, not by attempting original compositions. This wasn't meant to stifle creativity but to build a foundation of skills and patterns that could later support original work. By observing masters at work, apprentices could absorb not just techniques but also develop an eye for artistic problems. The implications are clear: if you want to learn something new, seek out examples of expert performance. Watch demonstrations, study worked examples, and pay attention to how experts approach problems before attempting to solve them yourself. This doesn't mean never practicing on your own—that's essential too—but it suggests that the traditional sequence of instruction followed by practice has solid cognitive foundations. Our extraordinary human capacity for learning from others is not a shortcut or a cheat—it's the very essence of how we build knowledge and skill.

Chapter 2: Finding the Sweet Spot: Optimal Challenge in Practice

Imagine trying to learn to play tennis by watching professional matches all day. You'd certainly see excellent technique, but without actually swinging a racket yourself, you'd never develop the skill. On the other hand, if you just hit balls randomly without any guidance or structure, your progress would be painfully slow. This highlights a fundamental tension in learning: we need both examples to follow and opportunities to practice, but finding the right balance between them is crucial. The concept of the "difficulty sweet spot" addresses this tension. When practice is too easy, we don't improve because we're not challenged. When it's too difficult, we become frustrated and overwhelmed. The ideal level of difficulty pushes us just beyond our current abilities—challenging enough to require effort but not so challenging that we can't make progress. Psychologists Robert and Elizabeth Bjork call these challenges "desirable difficulties"—learning situations that feel harder in the moment but lead to better long-term results. Science fiction writer Octavia Butler's journey illustrates this principle perfectly. As a young writer, she initially wrote imitative stories about "thirty-year-old white men who drank and smoked too much" because that's what she saw being published. But as she developed, she tackled increasingly difficult writing challenges—moving from short stories to novels, from simple plots to complex historical research for her masterpiece "Kindred." Writing never got easier for Butler; instead, she continuously sought out more challenging problems to solve. This progressive problem-solving was key to her development as one of the most celebrated science fiction authors of all time. What makes a difficulty "desirable" rather than simply frustrating? Research shows that certain types of difficulty enhance learning. Retrieving information from memory (rather than just reviewing it) strengthens our ability to recall it later—this is why flash cards are more effective than simply rereading notes. Similarly, spacing out practice sessions over time leads to better retention than cramming everything into one session. These difficulties are beneficial because they force our brains to work harder at encoding and retrieving information, which strengthens the neural pathways involved. Creating an effective practice loop involves cycling through three essential components: seeing examples, solving problems, and getting feedback. By repeatedly moving through this loop, we ensure all three ingredients of successful learning are available. As you progress in a skill, the practice loop can be made more challenging. Examples can fade away as you increasingly tackle problems using your internal reservoir of knowledge. The problems you choose can increase in complexity as you can manage extra cognitive load from bigger projects. Finally, self-assessment can play an increasing role over external feedback as you develop refined intuitions about what counts as excellent work. The key is to continuously adjust the difficulty level to maintain that sweet spot where learning happens most efficiently.

Chapter 3: The Specificity Principle: Why Skills Don't Fully Transfer

Have you ever wondered why someone can be brilliant at chess but average at business strategy? Or why a student who excels at math problems in class might struggle to apply those same concepts in real-world situations? The answer lies in what psychologists call the specificity principle—the idea that what we learn is surprisingly narrow and doesn't automatically transfer to other domains, even seemingly related ones. This principle challenges one of the oldest beliefs about education, known as the doctrine of formal discipline. Dating back to Plato, this view held that certain subjects like Latin or geometry trained general mental faculties such as memory or reasoning. The modern equivalent is the claim that brain-training games make you smarter overall, or that learning chess improves strategic thinking in all areas of life. It's an appealing idea—who wouldn't want to strengthen their mind like a muscle through targeted exercises? Unfortunately, extensive research suggests this mind-muscle analogy is deeply flawed. In a landmark series of experiments beginning in 1901, psychologist Edward Thorndike found that learning one skill rarely improved performance in unrelated areas. Students who practiced estimating the size of small rectangles showed only minimal improvement when estimating larger rectangles, and virtually no improvement when guessing weights or lengths. Thorndike concluded that "the mind is so specialized into a multitude of independent capacities that we alter human nature only in small spots." More recent research confirms this specificity. In 2016, Lumos Labs (creator of the brain-training program Lumosity) paid $2 million to settle Federal Trade Commission charges that they deceived customers with claims that their games would improve performance at work, school, and in everyday life. A massive study with over 11,000 participants found that while people improved at the specific games they practiced, there was "no evidence for transfer effects to untrained tasks, even when those tasks were cognitively closely related." So what actually happens when we learn? According to cognitive scientist John Anderson's ACT-R theory, skills are built from atomic units called production rules—if-then patterns that combine conditions with actions. These production rules can be abstract (like recognizing when to use a variable in algebra) and can involve mental actions (like setting subgoals), not just physical movements. This explains why some transfer does occur—a programmer who learns one language can learn a second one faster because many abstract concepts transfer, even if the specific syntax differs. The specificity principle has important practical implications. First, focus on the tasks you actually want to improve at rather than hoping for broad transfer. If you want to get better at public speaking, practice public speaking—playing chess won't help. Second, abstract skills require concrete examples to become useful. Teaching principles in their most generic form may help transfer, but students need multiple examples to appreciate the full range of applications. Finally, learn things for their own sake. Chess is a wonderful game with a rich history—it doesn't need the false promise of making you a better business strategist to be worth learning.

Chapter 4: Variability Over Repetition: Building Flexible Abilities

Imagine a jazz musician improvising a solo—creating something new and beautiful in real time, never playing exactly the same notes twice. How do musicians develop this remarkable flexibility? The answer lies not in mindless repetition but in embracing variability in practice. In the early 1940s, at Minton's Playhouse in Harlem, musicians like Charlie Parker, Dizzy Gillespie, and Thelonious Monk developed bebop, a revolutionary style of jazz centered on improvisation. These musicians weren't just playing memorized pieces—they were creating spontaneous, complex solos that required both technical mastery and creative flexibility. As trumpeter Wynton Marsalis explains, "Jazz is not just, 'Well, man, this is what I feel like playing.' It's a very structured thing that comes down from a tradition and requires a lot of thought and study." Research in motor learning reveals why varied practice leads to more flexible skills. Psychologist William Battig observed the "somewhat paradoxical principle" that training conditions which produced greater interference between items studied, and thus worse immediate performance, often accelerated learning for new tasks. This effect, known as contextual interference, has been demonstrated across many domains. In one experiment, subjects learning to knock down wooden barriers performed better during practice when they worked on one sequence at a time. However, when later tested on novel sequences, the group that had practiced in a random order performed significantly better. One explanation for this benefit is that varied practice helps develop control processes for deciding which action to take. When practice is highly predictable, we don't develop the ability to choose between different possible actions—a crucial skill for improvisation. Evidence for this comes from studies showing that the benefits of randomized practice are higher when choosing between different movements rather than executing the same movement with varying intensities. Variability also helps us generate abstractions—recognizing patterns across seemingly different examples. Jazz musicians develop this ability through extensive listening and transcription. Trumpeter Tommy Turrentine recalls a teacher who would sustain a note on the piano and ask him to remember it, then hit iron poles along their walk home to test if Turrentine could identify the notes. Such training develops the ability to recognize musical patterns across different contexts. As composer Chuck Israels remarks, an "essential ingredient in learning to be a musician is the ability to recognize a parallel case when you're confronted with one." Having multiple ways to represent the same knowledge provides another source of flexibility. Barry Harris, a renowned jazz pianist, observed that "the more ways you have of thinking about music, the more things you have to play in your solos." Some musicians understand music primarily through their ear, while others rely more on theoretical knowledge. The most flexible performers develop both systems. Physicist Richard Feynman similarly noted that "any theoretical physicist that's any good knows six or seven different theoretical representations for exactly the same physics"—different ways of thinking about the same problem that suggest different approaches.

Chapter 5: Quality From Quantity: Why Volume Drives Mastery

Thomas Edison was arguably the most inventive person in history, with 1,093 patents to his name. His creations transformed modern life—from the practical electric lightbulb to the phonograph, motion picture camera, and even the entire electric power industry. What made Edison so extraordinarily creative? Was it rare genius, or something more accessible? Research suggests a surprising answer: the most accomplished creators in any field tend to be the most prolific. Psychologist Dean Simonton has gathered extensive evidence showing that across domains, from science to art to literature, the people who produce the most work also produce the most masterpieces. Even more intriguing, the ratio of hits to total attempts remains roughly constant throughout a creator's career. This "equal-odds baseline" suggests that once a person begins contributing original work to their field, every attempt has roughly equal potential for impact. This pattern appears consistently across creative domains. Historians have found that the most highly cited scientists write almost twice as many papers as similar but less influential colleagues. The citations per paper don't increase with productivity, but since more productive authors write more papers, they're more likely to produce highly influential work. Price's law, named after historian of science Derek John de Solla Price, finds that in any field, approximately the square root of the total number of contributors produces half of all the output. In a field with 100 researchers, about 10 of them will produce half of all publications. Why does this quantity-quality relationship exist? Three explanations help us understand creative accomplishment: expertise, environment, and randomness. Expertise is certainly necessary—Edison's inventions built on his deep knowledge of electrical circuitry, and studies show that masterworks in music, art, and literature typically emerge only after years of practice. The cultural environment also matters greatly—ideas are products of their time, as evidenced by the surprising frequency of multiple independent discoveries like calculus (Newton and Leibniz) or evolution (Darwin and Wallace). But perhaps most importantly, chance plays a crucial role in creativity. Psychologist Donald Campbell proposed that creative thinking works like biological evolution—through blind variation with selective retention. Just as evolution produces incredible diversity through random mutation and natural selection, creativity might simply be a process of generating many ideas and keeping only the best ones. The long history of accidental inventions supports this view—penicillin, saccharin, superglue, and many other innovations were stumbled upon rather than deliberately designed. Edison himself embraced this trial-and-error approach. When developing his lightbulb, he tested thousands of materials before finding that carbonized bamboo worked best. After months of failed experiments on a battery design, a friend offered sympathy for his lack of results. Edison replied, "Why, man, I've got a lot of results. I know several thousand things that won't work!" The practical implication is clear: if you want to do creative work, you need to produce a lot of it. This doesn't mean churning out low-quality work—Edison maintained high standards—but rather taking many chances and being willing to discard ideas that don't measure up.

Chapter 6: Feedback Loops: Calibrating Knowledge Through Experience

Imagine trying to improve your golf swing without ever seeing where the ball lands. Or learning to cook without tasting your food. It would be nearly impossible to get better. This highlights a fundamental truth about learning: without feedback, improvement is often impossible. But not all feedback is created equal—its quality, timing, and interpretation dramatically affect how much we learn from experience. Consider the contrasting cases of poker players and clinical psychologists. Professional poker players like Annette Obrestad, who won a World Series of Poker tournament at age 19, develop remarkable expertise despite the game's inherent randomness. They do this by using probability theory to calculate correct decisions regardless of individual outcomes, and by analyzing thousands of hands through software that provides detailed feedback on their play patterns. In contrast, psychologist Paul Meehl found that clinical psychologists, despite years of experience making predictions about patients, often performed no better than simple statistical formulas. In some cases, more experience even led to worse judgments. Why does expertise develop reliably in poker but not in clinical prediction? Psychologists Daniel Kahneman and Gary Klein, who studied this question extensively, concluded that two conditions must be satisfied for genuine expertise to develop: "First, the environment must provide adequately valid cues to the nature of the situation. Second, people must have an opportunity to learn the relevant cues." Poker meets these conditions—the cards follow stable probability rules, and players can analyze thousands of hands to learn patterns. Clinical prediction often fails these tests—human behavior has complex causes, feedback is delayed or ambiguous, and clinicians may see relatively few cases with similar characteristics. This distinction between "kind" and "wicked" learning environments explains why experience doesn't always lead to expertise. In kind environments, patterns are consistent and feedback is clear and immediate. In wicked environments, patterns are complex or inconsistent, feedback is delayed or misleading, and the rules may change over time. Many important domains in life—from investing to medicine to politics—have wicked elements that make learning from experience difficult. Can we tame wicked learning environments to make them more learnable? Psychologist Philip Tetlock's research on forecasting political events offers some hope. In his Expert Political Judgment Project, he found that most expert predictions barely outperformed chance. However, he identified a subset of "superforecasters" who consistently made more accurate predictions. These exceptional forecasters shared several practices: they broke complex questions into smaller components, used base rates to establish starting probabilities, formed discussion groups to aggregate diverse perspectives, and kept precise score of their predictions to calibrate their confidence. These findings suggest strategies for improving our learning in uncertain environments. First, use models or simple calculations rather than relying solely on intuition—even simple statistical approaches often outperform subjective judgment. Second, enhance the quality of feedback by tracking decisions and outcomes systematically rather than relying on memory. Third, build a "brain trust" of diverse perspectives to challenge your thinking and aggregate more information. Finally, know when to trust your gut and when to be more cautious—intuition works best with clear cues and rapid feedback, while explicit reasoning is needed when those conditions aren't met.

Chapter 7: The Challenge of Unlearning: Overcoming Outdated Knowledge

When Tiger Woods was at the peak of his career, having just won the Masters tournament by a record-breaking twelve strokes, he did something that shocked the golfing world: he completely rebuilt his swing. Despite his phenomenal success, Woods recognized that his technique had flaws that might limit his long-term potential. The decision was incredibly risky—other professional golfers had seen their careers collapse after similar attempts to change their swings. Yet Woods persisted through eighteen months of grueling practice and temporarily worse performance before emerging with an even more dominant game. Woods's story illustrates a profound truth about learning: sometimes we need to get worse before we can get better. Improvement isn't always a straight line upward—it often requires unlearning habits or ideas that are holding us back. This process of unlearning is challenging because our brains are designed to automate skills through practice, making them increasingly resistant to change. Psychologists Paul Fitts and Michael Posner proposed that skill development occurs in three phases. In the cognitive phase, we consciously think about each movement or decision. In the associative phase, we refine our technique through practice. Finally, in the autonomous phase, the skill becomes automatic and requires little conscious attention. This progression is normally beneficial—it frees up mental resources and allows for smoother performance. But it creates a dilemma when we need to change an established pattern: we must temporarily regress to the cognitive phase, making our performance more effortful and error-prone. The challenge of unlearning extends beyond physical skills to our mental models and problem-solving approaches. Psychologist Abraham Luchins demonstrated this with his water jar experiments. After solving several puzzles using a complex formula (B - A - 2C), participants couldn't see a simpler solution (A - C) when it became available. This "Einstellung effect" shows how success with one approach can blind us to alternatives, even when they're more efficient. Similarly, Karl Duncker's candle problem revealed "functional fixedness"—our tendency to see objects only in their typical role. When subjects were given a box containing tacks and asked to mount a candle on a wall, few thought to use the box itself as a platform. But when the same materials were presented separately, most found the solution easily. Our prior experience with objects shapes how we perceive their potential uses. These cognitive biases create particular challenges in education. Students learning physics, economics, or psychology encounter theories that often contradict their intuitive understanding of the world. Research shows that many students learn to solve textbook problems correctly but continue using their pre-existing mental models outside the classroom. They think like Newton on the test but like Aristotle in everyday life. The process of unlearning requires several steps: recognizing that current knowledge or skills are inadequate, being willing to temporarily perform worse, receiving accurate feedback about progress, and practicing sufficiently to automate the new approach. It also requires patience—the autonomous phase can't be rushed, and old habits tend to resurface under pressure or fatigue until the new patterns are thoroughly established.

Summary

At its core, The Learning Matrix reveals that effective learning involves a dynamic interplay between three essential elements: seeing examples from others, engaging in deliberate practice, and receiving quality feedback. This framework helps explain why some learning environments produce rapid improvement while others lead to stagnation despite years of experience. The most powerful insight from this exploration is that learning is not merely an individual cognitive process but a socially embedded activity shaped by our extraordinary human capacity to learn from others. Whether we're examining the evolution of Tetris players, the training of pilots, or the development of jazz improvisation, we see that access to examples, opportunities for varied practice, and reliable feedback loops determine how quickly and effectively we develop expertise. This perspective challenges us to rethink common assumptions about learning: Is struggling alone always the best path to mastery? How can we design learning environments that provide the right balance of challenge and support? What might we achieve if we approached learning not as a solitary journey but as a collaborative process of building on the achievements of others?

About Author

Loading...

Scott H. Young

Read more

Related Books by Scott H. Young

Loading...

Ultralearning

James Clear

Accelerate Your Career, Master Hard Tasks and Outsmart the Competition

★★★★½4.5