Stanislas Dehaene > Quotes

“Introspection makes our conscious motives and strategies transparent to us, while we have no sure means of deciphering them in others. Yet we never genuinely know our true selves. We remain largely ignorant of the actual unconscious determinants of our behavior, and therefore we cannot accurately predict what our behavior will be in circumstances beyond the safety zone of our past experience. The Greek motto “Know thyself,” when applied to the minute details of our behavior, remains an inaccessible ideal. Our “self” is just a database that gets filled in through our social experiences, in the same format with which we attempt to understand other minds, and therefore it is just as likely to include glaring gaps, misunderstandings, and delusions.”
― Stanislas Dehaene, Consciousness and the Brain: Deciphering How the Brain Codes Our Thoughts

“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

“I do not mean, of course, that we can always accurately express our conscious thoughts with Proustian accuracy. Consciousness overflows language: we perceive vastly more than we can describe.”
― Stanislas Dehaene, Consciousness and the Brain: Deciphering How the Brain Codes Our Thoughts

“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

“Vladimir Nabokov, in his Lectures on Literature (1980), saw it all: Literature was not born the day when a boy crying “wolf, wolf” came running out of the Neanderthal valley with a big gray wolf at his heels; literature was born on the day when a boy came crying “wolf, wolf” and there was no wolf behind him.”
― Stanislas Dehaene, Consciousness and the Brain: Deciphering How the Brain Codes Our Thoughts

“quite opposite to Descartes’s organ metaphor, our global neuronal workspace does not operate in an input-output manner, waiting to be stimulated before producing its outputs. On the contrary, even in full darkness, it ceaselessly broadcasts global patterns of neural activity, causing what William James called the “stream of consciousness”—an uninterrupted flow of loosely connected thoughts, primarily shaped by our current goals and only occasionally seeking information in the senses. René Descartes could not have imagined a machine of this sort, where intentions, thoughts, and plans continually pop up to shape our behavior. The outcome, I argue, is a “free-willing” machine that resolves Descartes’s challenge”
― Stanislas Dehaene, Consciousness and the Brain: Deciphering How the Brain Codes Our Thoughts

“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

“Science often progresses by carving out new distinctions that refine the fuzzy categories of natural language.”
― Stanislas Dehaene, Consciousness and the Brain: Deciphering How the Brain Codes Our Thoughts

“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

“Literature was not born the day when a boy crying “wolf, wolf” came running out of the Neanderthal valley with a big gray wolf at his heels; literature was born on the day when a boy came crying “wolf, wolf” and there was no wolf behind him. Consciousness”
― Stanislas Dehaene, Consciousness and the Brain: Deciphering How the Brain Codes Our Thoughts

“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

“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

“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

“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

“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

“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

“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

“In the human species, the peak of synaptic overproduction ends around two years of age in the visual cortex, three or four years of age in the auditory cortex, and between five and ten years of age in the prefrontal cortex.”
― Stanislas Dehaene, How We Learn: Why Brains Learn Better Than Any Machine . . . for Now

“Teachers who manage to mobilize all four functions in their students will undoubtedly maximize the speed and efficiency with which their classes can learn.”
― Stanislas Dehaene, How We Learn: Why Brains Learn Better Than Any Machine . . . for Now

“Education is the main accelerator of our brain.”
― Stanislas Dehaene, How We Learn: Why Brains Learn Better Than Any Machine . . . for Now

“All infants are genial linguists: as early as eighteen months of age, they easily acquire ten to twenty words a day—but only if they are spoken to.”
― Stanislas Dehaene, How We Learn: Why Brains Learn Better Than Any Machine . . . for Now

“A new field of science was emerging: mathematical cognition, or the scientific inquiry into how the human brain gives rise to mathematics... the transition from mathematics to neuropsychology.”
― Stanislas Dehaene

“The expansion of one stops the other—and because letters settle down in the left hemisphere, which is dominant for language, faces have no choice but to move to the right side.”
― Stanislas Dehaene, How We Learn: Why Brains Learn Better Than Any Machine . . . for Now

“quintessence of learning: being able to adapt to unpredictable conditions as quickly as possible.”
― 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

“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

“Very slowly, the representation of faces changed: as the children became more and more literate, face responses increased in the right hemisphere, in direct proportion to reading scores.”
― Stanislas Dehaene, How We Learn: Why Brains Learn Better Than Any Machine . . . for Now