How We Learn Quotes

“Amazingly, most teachers receive little or no professional training in the science of learning. My feeling is that we should urgently change this state of affairs, because we now possess considerable scientific knowledge about the brain’s learning algorithms and the pedagogies that are the most efficient.”
― Stanislas Dehaene, How We Learn: Why Brains Learn Better Than Any Machine . . . for Now

“The moral here is that nature and nurture should not be opposed. Pure learning, in the absence of any innate constraints, simply does not exist.”
― Stanislas Dehaene, How We Learn: Why Brains Learn Better Than Any Machine . . . for Now

“These pillars are: Attention, which amplifies the information we focus on. Active engagement, an algorithm also called “curiosity,” which encourages our brain to ceaselessly test new hypotheses. Error feedback, which compares our predictions with reality and corrects our models of the world. Consolidation, which renders what we have learned fully automated and involves sleep as a key component”
― Stanislas Dehaene, How We Learn: Why Brains Learn Better Than Any Machine . . . for Now

“Thanks to this predictive learning mechanism, arbitrary signals can become the bearers of reward and trigger a dopamine response. This secondary reward effect has been demonstrated with money in humans and with the mere sight of a syringe in drug addicts. In both cases, the brain anticipates future rewards. As we saw in the first chapter, such a predictive signal is extremely useful for learning, because it allows the system to criticize itself and to foresee the success or failure of an action without having to wait for external confirmation.”
― Stanislas Dehaene, How We Learn: Why Brains Learn Better Than Any Machine . . . for Now

“No surprise, no learning: this basic rule now seems to have been validated in all kinds of organisms—including young children. Remember that surprise is one of the basic indicators of babies’ early skills: they stare longer at any display that magically presents them with surprising events that violate the laws of physics, arithmetic, probability, or psychology (see figure on this page and figure 5 in the color insert). But children do not just stare every time they are surprised: they demonstrably learn.”
― Stanislas Dehaene, How We Learn: Why Brains Learn Better Than Any Machine . . . for Now

“Whatever input a brain region cannot explain is therefore passed on to the next level, which then attempts to make sense of it. We may conceive of the cortex as a massive hierarchy of predictive systems, each of which tries to explain the inputs and exchanges the remaining error messages with the others, in the hope that they may do a better job.”
― Stanislas Dehaene, How We Learn: Why Brains Learn Better Than Any Machine . . . for Now

“Curiosity is therefore a force that encourages us to explore. From this perspective, it resembles the drive for food or sexual partners, except that it is motivated by an intangible value: the acquisition of information.”
― Stanislas Dehaene, How We Learn: Why Brains Learn Better Than Any Machine . . . for Now

“Do I dare set forth here,” writes Rousseau in Emile, or On Education, “the most important, the most useful rule of all education? It is not to save time, but to squander it.” For Rousseau and his successors, it is always better to let children discover for themselves and build their own knowledge, even if it implies that they might waste hours tinkering and exploring. . . . This time is never lost, Rousseau believed, because it eventually yields autonomous minds, capable not only of thinking for themselves but also of solving real problems, rather than passively receiving knowledge and spitting out rote and ready-made solutions. “Teach your student to observe the phenomena of nature,” says Rousseau, “and you will soon rouse his curiosity; but if you want his curiosity to grow, do not be in too great a hurry to satisfy it. Lay the problems before him and let him solve them himself.”
― Stanislas Dehaene, How We Learn: Why Brains Learn Better Than Any Machine . . . for Now

“So, does literacy lead to a knockout or a blockade of the cortex? Our experiments suggest the latter: learning to read blocks the growth of face-recognition areas in the left hemisphere.”
― Stanislas Dehaene, How We Learn: Why Brains Learn Better Than Any Machine . . . for Now

“But we can also ask the opposite question: Are there regions that are more active among bad readers and whose activity decreases as one learns to read? The answer is positive: in illiterates, the brain’s responses to faces are more intense. The better we read, the more this activity decreases in the left hemisphere, at the exact place in the cortex where written words find their niche—the brain’s letter box area. It’s as if the brain needs to make room for letters in the cortex, so the acquisition of reading interferes with the prior function of this region, which is the recognition of faces and objects.”
― Stanislas Dehaene, How We Learn: Why Brains Learn Better Than Any Machine . . . for Now

“This is a revolution: for millions of years, evolution had been content with fuzzy quantities. Symbol learning is a powerful factor for change: with education, all our brain circuits are repurposed to allow for the manipulation of exact numbers.”
― Stanislas Dehaene, How We Learn: Why Brains Learn Better Than Any Machine . . . for Now

“Our brain is therefore not simply passively subjected to sensory inputs. From the get-go, it already possesses a set of abstract hypotheses, an accumulated wisdom that emerged through the sift of Darwinian evolution and which it now projects onto the outside world. Not all scientists agree with this idea, but I consider it a central point: the naive empiricist philosophy underlying many of today's artificial neural networks is wrong. It is simply not true that we are born with completely disorganized circuits devoid of any knowledge, which later receive the imprint of their environment. Learning, in man and machine, always starts from a set of a priori hypotheses, which are projected onto the incoming data, and from which the system selects those that are best suited to the current environment. As Jean-Pierre Changeux stated in his best-selling book Neuronal Man (1985), “To learn is to eliminate.”
― Stanislas Dehaene, How We Learn: Why Brains Learn Better Than Any Machine . . . for Now

“Yann LeCun's strategy provides a good example of a much more general notion: the exploitation of innate knowledge. Convolutional neural networks learn better and faster than other types of neural networks because they do not learn everything. They incorporate, in their very architecture, a strong hypothesis: what I learn in one place can be generalized everywhere else.

The main problem with image recognition is invariance: I have to recognize an object, whatever its position and size, even if it moves to the right or left, farther or closer. It is a challenge, but it is also a very strong constraint: I can expect the very same clues to help me recognize a face anywhere in space. By replicating the same algorithm everywhere, convolutional networks effectively exploit this constraint: they integrate it into their very structure. Innately, prior to any learning, the system already “knows” this key property of the visual world. It does not learn invariance, but assumes it a priori and uses it to reduce the learning space-clever indeed!”
― Stanislas Dehaene, How We Learn: Why Brains Learn Better Than Any Machine . . . for Now

“Being active and engaged does not mean that the body must move. Active engagement takes place in our brains, not our feet. The brain learns efficiently only if it is attentive, focused, and active in generating mental models.”
― Stanislas Dehaene, How We Learn: Why Brains Learn Better Than Any Machine . . . for Now

“One group was told to spend all their time studying, in eight short sessions. A second group received six sessions of studying, interrupted by two tests. Finally, the third group alternated four brief study sessions and four tests. Because all three groups had the same amount of time, testing actually reduced the time available for studying. Yet the results were clear: forty-eight hours later, the students’ memory of the word list was better the more opportunities they had to test themselves. Regularly alternating periods of studying and testing forced them to engage and receive explicit feedback (“I know this word now, but it’s this other one I can never remember . . .”). Such self-awareness, or “meta-memory,” is useful because it allows the learner to focus harder on the difficult items during the subsequent study sessions.21 The effect is clear: the more you test yourself, the better you remember what you have to learn.”
― Stanislas Dehaene, How We Learn: Why Brains Learn Better Than Any Machine . . . for Now

“We cannot ignore the tremendous negative effects that bad grades have on the emotional systems of the brain: discouragement, stigmatization, feelings of helplessness. . . . Let us listen to the insightful voice of a professional dunce: Daniel Pennac, today a leading French writer who received the famous Renaudot Prize in 2007 for his book School Blues, but who was at the bottom of his class year after year:”
― Stanislas Dehaene, How We Learn: Why Brains Learn Better Than Any Machine . . . for Now

“Grades can also be profoundly unfair, especially for students who are unable to keep up, because the level of the exams usually increases from week to week. Let’s take the analogy of video games. When you discover a new game, you initially have no idea how to progress effectively. Above all, you don’t want to be constantly reminded of how bad you are! That’s why video game designers start with extremely easy levels, where you are almost sure to win. Very gradually, the difficulty increases and, with it, the risk of failure and frustration—but programmers know how to mitigate this by mixing the easy with the difficult, and by leaving you free to retry the same level as many times as you need. You see your score steadily increase . . . and finally, the joyous day comes when you successfully pass the final level, where you were stuck for so long. Now compare this with the report cards of “bad” students: they start the year off with a bad grade, and instead of motivating them by letting them take the same test again until they pass, the teacher gives them a new exercise every week, almost always beyond their abilities. Week after week, their “score” hovers around zero. In the video game market, such a design would be a complete disaster. All too often, schools use grades as punishments.”
― Stanislas Dehaene, How We Learn: Why Brains Learn Better Than Any Machine . . . for Now

“Not only are they imprecise, but they are also often delayed by several weeks, at which point most students have long forgotten which aspects of their inner reasoning misled them.”
― Stanislas Dehaene, How We Learn: Why Brains Learn Better Than Any Machine . . . for Now

“According to learning theory, a grade is just a reward (or punishment!) signal. However, one of its obvious shortcomings is that it is totally lacking in precision. The grade of an exam is usually just a simple sum—and as such, it summarizes different sources of errors without distinguishing them. It is therefore insufficiently informative: by itself, it says nothing about the reason why we made a mistake, or how to correct ourselves. In the most extreme case, an F that stays an F provides zero information, only the clear social stigma of incompetence.”
― Stanislas Dehaene, How We Learn: Why Brains Learn Better Than Any Machine . . . for Now

“So, let us spare them this distress and give them the most neutral and informative feedback possible. Error feedback should not be confused with punishment.”
― Stanislas Dehaene, How We Learn: Why Brains Learn Better Than Any Machine . . . for Now

“Take, for example, the following sentence: “I prefer to eat with a fork and a camel.” Your brain has just generated an N400 wave, an error signal evoked by a word or an image which is incompatible with the preceding context.11 As its name suggests, this is a negative response that occurs at about four hundred milliseconds after the anomaly and arises from neuronal populations of the left temporal cortex that are sensitive to word meaning. On the other hand, Broca’s area in the inferior prefrontal cortex reacts to errors of syntax, when the brain predicts a certain category of word and receives another,12 as in the following sentence: “Don’t hesitate to take your whenever medication you feel sick.” This time, just after the unexpected word “whenever,” the areas of your brain that specialize in syntax emitted a negative wave immediately followed by a P600 wave—a positive peak that occurs around six hundred milliseconds. This response indicates that your brain detected a grammar error and is trying to repair it.”
― Stanislas Dehaene, How We Learn: Why Brains Learn Better Than Any Machine . . . for Now

“The auditory cortex seems to perform a simple calculation: it uses the recent past to predict the future. As soon as a note or a group of notes repeats, this region concludes that it will continue to do so in the future. This is useful because it keeps us from paying too much attention to boring, predictable signals. Any sound that repeats is squashed at the input side, because its incoming activity is canceled by an accurate prediction. As long as the input sensory signal matches the prediction that the brain generates, the difference is zero, and no error signal gets propagated to higher-level brain regions. Subtracting the prediction shuts down the incoming inputs—but only as long as they are predictable. Any sound that violates our brain’s expectations, on the contrary, is amplified. Thus, the simple circuit of the auditory cortex acts as a filter: it transmits to the higher levels of the cortex only the surprising and unpredictable information which it cannot explain by itself.”
― Stanislas Dehaene, How We Learn: Why Brains Learn Better Than Any Machine . . . for Now

“Let’s start with an elementary example: Imagine hearing a series of identical notes, A A A A A. Each note elicits a response in the auditory areas of your brain—but as the notes repeat, those responses progressively decrease. This is called “adaptation,” a deceptively simple phenomenon that shows that your brain is learning to predict the next event. Suddenly, the note changes: A A A A A#. Your primary auditory cortex immediately shows a strong surprise reaction: not only does the adaptation fade away, but additional neurons begin to vigorously fire in response to the unexpected sound. And it is not just repetition that leads to adaptation: what matters is whether the notes are predictable. For instance, if you hear an alternating set of notes, such as A B A B A, your brain gets used to this alternation, and the activity in your auditory areas again decreases. This time, however, it is an unexpected repetition, such as A B A B B, that triggers a surprise response.”
― Stanislas Dehaene, How We Learn: Why Brains Learn Better Than Any Machine . . . for Now

“Forward blocking provides one of the most spectacular refutations of the associationist view.5 In blocking experiments, an animal is given two sensory clues, say, a bell and a light, both of which predict the imminent arrival of food. The trick is to present them sequentially. We start with the light: the animal learns that whenever the light is on, it predicts the arrival of food. Only then do we introduce dual trials where both light and bell predict food. Finally, we test the effect of the bell alone. Surprise: it has no effect whatsoever! Upon hearing the bell, the animal does not salivate; it seems utterly oblivious to the repeated association between the bell and the food reward. What happened? The finding is incompatible with associationism, but it fits perfectly with the Rescorla-Wagner theory. The key idea is that the acquisition of the first association (light and food) blocked the second one (bell and food). Why? Because the prediction based on light alone suffices to explain everything.”
― Stanislas Dehaene, How We Learn: Why Brains Learn Better Than Any Machine . . . for Now

“I have no special talent. I am only passionately curious. Albert Einstein (1952)”
― Stanislas Dehaene, How We Learn: Why Brains Learn Better Than Any Machine . . . for Now

“In adulthood, this social conformism persists and grows. Even the most trivial of our perceptual decisions, such as judging the length of a line, are influenced by social context: when our neighbors come to a different conclusion than us, we frequently revise our judgment to align it with theirs, even when their answer seems implausible.47 In such cases, the social animal in us overrides the rational beast.”
― Stanislas Dehaene, How We Learn: Why Brains Learn Better Than Any Machine . . . for Now

“But Homo sapiens’ dependency on social communication and education is as much of a curse as it is a gift. On the flip side of the coin, it is education’s fault that religious myths and fake news propagate so easily in human societies. From the earliest age, our brains trustfully absorb the tales we are told, whether they are true or false. In a social context, our brains lower their guard; we stop acting like budding scientists and become mindless lemmings. This can be good—as when we trust the knowledge of our science teachers, and thus avoid having to replicate every experiment since Galileo’s time! But it can also be detrimental, as when we collectively propagate an unreliable piece of “wisdom” inherited from our forebears. It is on this basis that doctors foolishly practiced bloodletting and cupping therapies for centuries, without ever testing their actual impact. (In case you are wondering, both are actually harmful in the vast majority of diseases.)”
― Stanislas Dehaene, How We Learn: Why Brains Learn Better Than Any Machine . . . for Now

“It is not only eye contact that matters: children also quickly understand the communicative intention that lies behind the act of pointing with a finger (whereas chimpanzees never really understand this gesture).”
― Stanislas Dehaene, How We Learn: Why Brains Learn Better Than Any Machine . . . for Now

“I have already told you about experiments where babies are taught the meaning of a new word, such as “wog.” If the infants can follow the speaker’s gaze toward the so-called wog, they have no trouble learning this word in just a few trials—but if wog is repeatedly emitted by a loudspeaker, in direct relation to the same object, no learning occurs. The same goes for learning phonetic categories: a nine-month-old American child who interacts with a Chinese nanny for only a few weeks acquires Chinese phonemes—but if he receives exactly the same amount of linguistic stimulation from a very high-quality video, no learning occurs.”
― Stanislas Dehaene, How We Learn: Why Brains Learn Better Than Any Machine . . . for Now

“The art of paying attention, the great art,” says the philosopher Alain (1868–1951), “supposes the art of not paying attention, which is the royal art.”
― Stanislas Dehaene, How We Learn: Why Brains Learn Better Than Any Machine . . . for Now