Category: Learning

The Feynman Technique: The Best Way to Learn Anything

There are four simple steps to the Feynman Technique, which I'll explain below:

  1. Choose a Concept
  2. Teach it to a Toddler
  3. Identify Gaps and Go Back to The Source Material
  4. Review and Simplify (optional)


If you're not learning you're standing still. So what's the best way to learn new subjects and identify gaps in our existing knowledge?

Two Types of Knowledge

There are two types of knowledge and most of us focus on the wrong one. The first type of knowledge focuses on knowing the name of something. The second focuses on knowing something. These are not the same thing. The famous Nobel winning physicist Richard Feynman understood the difference between knowing something and knowing the name of something and it's one of the most important reasons for his success. In fact, he created a formula for learning that ensured he understood something better than everyone else.

It's called the Feynman Technique and it will help you learn anything faster and with greater understanding. Best of all, it's incredibly easy to implement.

“The person who says he knows what he thinks but cannot express it usually does not know what he thinks.”

— Mortimer Adler

There are four steps to the Feynman Technique.

Step 1: Teach it to a child

Take out a blank sheet of paper and write the subject you want to learn at the top. Write out what you know about the subject as if you were teaching it to a child. Not your smart adult friend but rather an 8-year-old who has just enough vocabulary and attention span to understand basic concepts and relationships.

A lot of people tend to use complicated vocabulary and jargon to mask when they don’t understand something. The problem is we only fool ourselves because we don’t know that we don’t understand. In addition, using jargon conceals our misunderstanding from those around us.

When you write out an idea from start to finish in simple language that a child can understand (tip: use only the most common words), you force yourself to understand the concept at a deeper level and simplify relationships and connections between ideas. If you struggle, you have a clear understanding of where you have some gaps. That tension is good –it heralds an opportunity to learn.

Step 2: Review

In step one, you will inevitably encounter gaps in your knowledge where you’re forgetting something important, are not able to explain it, or simply have trouble connecting an important concept.
This is invaluable feedback because you’ve discovered the edge of your knowledge. Competence is knowing the limit of your abilities, and you’ve just identified one!
This is where the learning starts. Now you know where you got stuck, go back to the source material and re-learn it until you can explain it in basic terms.
Identifying the boundaries of your understanding also limits the mistakes you’re liable to make and increases your chance of success when applying knowledge.

Step 3: Organize and Simplify

Now you have a set of hand-crafted notes. Review them to make sure you didn’t mistakenly borrow any of the jargon from the source material. Organize them into a simple story that flows.
Read them out loud. If the explanation isn’t simple or sounds confusing that’s a good indication that your understanding in that area still needs some work.

Step 4 (optional): Transmit

If you really want to be sure of your understanding, run it past someone (ideally who knows little of the subject –or find that 8-year-old!). The ultimate test of your knowledge is your capacity to convey it to another.


Not only is this a wonderful recipe for learning but it's also a window into a different way of thinking that allows you to tear ideas apart and reconstruct them from the ground up. (Elon Musk calls this thinking from first principles.) This leads to a much deeper understanding of the ideas and concepts. Importantly, approaching problems in this way allows you to understand when others don't know what they are talking about.

Feynman's approach intuitively believes that intelligence is a process of growth, which dovetails nicely with the work of Carol Dweck, who beautifully describes the difference between a fixed and growth mindset.


Still Curious?

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Are You A Learning Machine?

The same advice from three remarkably different people: Become a Learning Machine.

Charlie Munger says:

I constantly see people rise in life who are not the smartest, sometimes not even the most diligent, but they are learning machines. They go to bed every night a little wiser than they were when they got up and boy does that help, particularly when you have a long run ahead of you.

Carol Dweck says:

You have to apply yourself each day to becoming a little better. By applying yourself to the task of becoming a little better each and every day over a period of time, you will become a lot better.

And this tidbit from Susan Cain, also fits:

…identify the tasks or knowledge that are just out of your reach, strive to upgrade your performance, monitor your progress, and revise accordingly.

Does Experience Make You an Expert?

In Experience and validity of clinical judgment: The illusory correlation, Robyn Dawes explores the relationship between experience and accuracy.

There is research about the relationship between experience and diagnostic and predictive accuracy, and about the validity of interviewing people to find out what they are like. Garb has recently summarized the research on experience and accuracy. There is no relationship between years of clinical experience and accuracy of judgment. A report of a task force of the American Psychological Association convened in the early 1980s noted that there was no evidence that professional competence is related to years of professional experience.

And yet we seek experienced people to be our teachers, executives, and political leaders.

Ben Franklin is oft quoted as saying “experience is the best teacher,” the second clause reads “and fools will learn from no other.” Only Franklin didn't say “the best teacher” he said “dear teacher,” which was clearly intended to mean expensive.

The 10,000-hour rule, popularized by Malcolm Gladwell and based on Anders Ericsson’s study, The Role of Deliberate Practice in the Acquisition of Expert Performance, states that in order to become an expert, one must have 10,000 hours of deliberate practice under their belts. This has been highly disputed by many, including Ericsson himself. There’s no question that practice is necessary for improvement, but 10,000 hours isn’t a magic number that wields the power of universal application.

Something else to ponder: why is it that we often forget to account for the length of time that an expert has been out of practice in their field?

So does experience really make you an expert? What does it actually mean to be one? It turns out, we don't learn from experience in many contexts.

The analysis of what we learn and why we learn it, however, quickly yields sobriety about embracing generalizations about the effect of experience on learning across all contexts. For example, learning to sit in a chair, become a chess grandmaster, make a correct medical diagnosis, or avoid a war are quite different processes. The word “learning” is, of course, common to all, but close examination reveals that it means little more than that someone with no experience whatsoever could not accomplish any of these tasks.

Dawes illuminates this highly contrarian idea through quite unremarkable human behaviours like sitting in a chair and driving.

What then are the differences? First, consider sitting in a chair. It is a motor skill. It is done automatically. It does not involve any conscious hypotheses. It is clearly learned through early experience that provides immediate feedback about failure. Finally, it is not taught in the sense that one person conveys a verbal or mathematical description to another about how to do it. (In fact an amusing exercise is to attempt to write such a description, convince somebody else to follow your instructions explicitly—and then watch the person fail.) Driving a car has many similar characteristics. For example, steering it in a straight line is accomplished by very tiny discrete adjustments of the steering wheel that are not accomplished consciously (Ehrlich, 1966). (The “weaving” behavior of drunk drivers is often due to the impairment of these movements, rather than to any visual problem.) The skills needed to perform these slight movements are attained only through experience driving; in fact, most complete novices on the first driving lesson alternate between going toward the ditch and almost crossing the center line—much to the surprise and consternation of their novice teachers, who themselves may be unaware of their own “tremorous” movements of the steering wheel. As with sitting in a chair, explicit verbal instructions to someone else about exactly how to drive a car could result in disaster for the person who follows them rigidly.

Consider the curious thing that happened during the Paris Wine Tasting of 1976, alternatively known as the Judgement of Paris (its name was inspired by a story in Greek mythology). During this blind taste competition, French wine experts judged ten different reds and ten different whites. Contrary to the strongly held belief that France produced the finest wines, it was the California wines that received the highest scores. Not only did the shocking results of the competition call into question the supposed superiority of French wine, but it served as a reason for people to wonder what authority an expert had over a casual wine drinker.

Abstract of Experience and validity of clinical judgment: The illusory correlation

Mental health experts often justify diagnostic and predictive judgments on the basis of “years of experience” with a particular type of person. Justification by experience is common in legal settings, and can have profound consequences for the person about whom such judgments are made. However, research shows that the validity of clinical judgment and amount of clinical experience are unrelated. The role of experience in learning varies as a function of what is to be learned. Experiments show that learning conceptual categories depends upon: (1) the learner's having clear hypotheses about the possible rule for category membership prior to receiving feedback about which instances belong to the category, and, (2) the systematic nature of such feedback, especially about erroneous categorizations. Since neither of these conditions is satisfied in clinical contexts in psychology, the subsequent failure of experience per se to enhance diagnostic or predictive validity is unsurprising. Claims that “I can tell on the basis of my experience with people of a particular type (e.g., child abusers) that this person is of that type (e.g., a child abuser)” are simply invalid.


Still curious? Try reading, The Ambiguities of Experience. If you want to learn more about Dawes, check out his book Everyday Irrationality: How Pseudo-Scientists, Lunatics, And The Rest Of Us Systematically Fail To Think Rationally.

Scientifically Proven Ways to Study Better

…Students might consider taking the questions in the back of the textbook chapter and try to answer them before reading the chapter. (If there are no questions, convert the section headings to questions. If the heading is Pavlovian Conditioning, ask yourself What is Pavlovian conditioning?). Then read the chapter and answer the questions while reading it. When the chapter is finished, go back to the questions and try answering them again. For any you miss, restudy that section of the chapter. Then wait a few days and try to answer the questions again (restudying when you need to). Keep this practice up on all the chapters you read before the exam and you will be have learned the material in a durable manner and be able to retrieve it long after you have left the course.

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Learning Effectively From Experience: Distinguishing High from Low Performers

High performers learn from both success and failure making small adjustments. Conversely, low performers learned more from success.

Learning effectively from experience is a daunting task for any organism. For every good or bad outcome, there are an immense number of potential causes and associations to be considered. For many decisions, it can be nearly impossible to pick out the few relevant factors from the many irrelevant factors, even with extensive experience. A major stumbling block for learning in these multi-dimensional environments is the tendency to form spurious beliefs: i.e., to attribute a causal role to factors that have no actual bearing on the outcome.

The formation of spurious beliefs is universal, from Skinner's observations of superstitious pigeons [1] to an athlete's belief in a lucky hat. In some situations, these beliefs are essentially harmless; by-products of learning mechanisms, but in other settings their impact can be severe. For example, spurious associations can have literal life-or-death consequences when they affect the complex decisions made by physicians. These expert decision-makers must extract and distill relevant features from a myriad of tests, symptoms, and personal histories, and employ these features to make critical medical decisions. Consequently, it is important to understand how spurious associations form and how they can bias subsequent decisions.

Spurious learning and false belief formation happens when the dorsolateral prefrontal cortex fails to distinguish correctly between important and unimportant associations.

The authors conclude:

High performers learned from both successes and failures, and made smaller rule adjustments after feedback. Conversely, low performers learned disproportionately from successes, and made larger rule adjustments. …

Taken together, the behavioral and neuroimaging results suggest that success-chasing and confirmation bias may underlie the relative pervasiveness of premature, asymmetric learning and the resultant poor performance of the majority of physician subjects in the present study. The general human bias towards confirmation over disconfirmation in hypothesis-testing has been extensively documented in a variety of non-medical contexts, such as the Wason Card Task. Conversely, the necessity for disconfirmation learning in empirical investigations is a key principle identified by the philosopher of science, Karl Popper [28]. Conceivably, providing medical professionals with formal training in disconfirmation learning could improve their ability to learn effectively from clinical experience in real-world settings. Exploring this possibility would be an important area for future research.

In conclusion, the results of this study show distinct patterns of learning, both behaviorally and neurally, between effective and ineffective learners among physicians making decisions in a medically framed learning task. The tendency to chase successes and ignore failures provides a simple computational model of how spurious beliefs might be formed, and how different individuals seeing similar data might learn very different sets of associations. The neural differences observed could conceivably be developed into useful biomarkers for essential differences in individual learning styles. These may in turn prove useful in identifying those individuals who can resist the impulse to chase successes, and hence learn most effectively from experience. Finally, we note that although this study focused upon the specific case of medical decision-making, the findings may be also be relevant to many other fields in which experts must make high-stakes decisions by drawing upon personal experience.


Accurate associative learning is often hindered by confirmation bias and success-chasing, which together can conspire to produce or solidify false beliefs in the decision-maker. We performed functional magnetic resonance imaging in 35 experienced physicians, while they learned to choose between two treatments in a series of virtual patient encounters. We estimated a learning model for each subject based on their observed behavior and this model divided clearly into high performers and low performers. The high performers showed small, but equal learning rates for both successes (positive outcomes) and failures (no response to the drug). In contrast, low performers showed very large and asymmetric learning rates, learning significantly more from successes than failures; a tendency that led to sub-optimal treatment choices. Consistently with these behavioral findings, high performers showed larger, more sustained BOLD responses to failed vs. successful outcomes in the dorsolateral prefrontal cortex and inferior parietal lobule while low performers displayed the opposite response profile. Furthermore, participants' learning asymmetry correlated with anticipatory activation in the nucleus accumbens at trial onset, well before outcome presentation. Subjects with anticipatory activation in the nucleus accumbens showed more success-chasing during learning. These results suggest that high performers' brains achieve better outcomes by attending to informative failures during training, rather than chasing the reward value of successes. The differential brain activations between high and low performers could potentially be developed into biomarkers to identify efficient learners on novel decision tasks, in medical or other contexts.


(via Deric Bownds)

Why are some people so much more effective at learning from their mistakes?

Jonah Lehrer comments on a new study forthcoming in Psychological Science led by Jason Moser at Michigan State that helps explain why some people are more effective at learning from their mistakes than others.

…the scientists applied a dichotomy first proposed by Carol Dweck, a psychologist at Stanford. In her influential research, Dweck distinguishes between people with a fixed mindset — they tend to agree with statements such as “You have a certain amount of intelligence and cannot do much to change it” — and those with a growth mindset, who believe that we can get better at almost anything, provided we invest the necessary time and energy. While people with a fixed mindset see mistakes as a dismal failure — a sign that we aren’t talented enough for the task in question — those with a growth mindset see mistakes as an essential precursor of knowledge, the engine of education.

On the Moser study, Lehrer comments, “It turned out that those subjects with a growth mindset were significantly better at learning from their mistakes. As a result, they showed a spike in accuracy immediately following an error. … implying that the extra awareness was paying dividends in performance. Because the subjects were thinking about what they got wrong, they learned how to get it right.”

Dweck's research, found mindsets have important practical implications. She debunked the commonly held belief that praise for ability encouraged motivation, concluding that “that praise for intelligence had more negative consequences for students' achievement motivation than praise for effort.” How you approach the problem makes a difference. “According to Dweck, praising kids for intelligence encourages them to “look” smart, which means that they shouldn’t risk making a mistake.”

So, praising for innate intelligence encourages kids to avoid learning activities where they are likely to fail. And unless we experience the unpleasantness of being wrong and direct our attention to the very thing we'd like to ignore the mind will never become effective at learning from mistakes. As Lehrer concludes, we'll keep making the same mistakes, “forsaking self-improvement for the sake of self-confidence.”

If you want to learn more, read Dweck's book Mindset: The New Psychology of Success.

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Jonah Lehrer is the author of How We Decide and Proust Was a Neuroscientist.