How We Learn: Why Brains Learn Better Than Any Machine . . . for Now - Hardcover

Dehaene, Stanislas

Hardcover

ISBN 10: 0525559884 ISBN 13: 9780525559887

Verlag: Viking, 2020

Inhaltsangabe

“There are words that are so familiar they obscure rather than illuminate the thing they mean, and ‘learning’ is such a word. It seems so ordinary, everyone does it. Actually it’s more of a black box, which Dehaene cracks open to reveal the awesome secrets within.”--The New York Times Book Review

An illuminating dive into the latest science on our brain's remarkable learning abilities and the potential of the machines we program to imitate them

The human brain is an extraordinary learning machine. Its ability to reprogram itself is unparalleled, and it remains the best source of inspiration for recent developments in artificial intelligence. But how do we learn? What innate biological foundations underlie our ability to acquire new information, and what principles modulate their efficiency?

In How We Learn, Stanislas Dehaene finds the boundary of computer science, neurobiology, and cognitive psychology to explain how learning really works and how to make the best use of the brain’s learning algorithms in our schools and universities, as well as in everyday life and at any age.

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Stanislas Dehaene is the director of the Cognitive Neuroimaging Unit in Saclay, France, and the professor of experimental cognitive psychology at the Collège de France. He is currently the president of the Scientific Council of the French Ministry of Education.

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Chapter 1

Seven Definitions of Learning



What does "learning" mean? My first and most general definition is the following: to learn is to form an internal model of the external world.



You may not be aware of it, but your brain has acquired thousands of internal models of the outside world. Metaphorically speaking, they are like miniature mock-ups more or less faithful to the reality they represent. We all have in our brains, for example, a mental map of our neighborhood and our home-all we have to do is close our eyes and envision them with our thoughts. Obviously, none of us were born with this mental map-we had to acquire it through learning.



The richness of these mental models, which are, for the most part, unconscious, exceeds our imagination. For example, you possess a vast mental model of the English language, which allows you to understand the words you are reading right now and guess that plastovski is not an English word, whereas swoon and wistful are, and dragostan could be. Your brain also includes several models of your body: it constantly uses them to map the position of your limbs and to direct them while maintaining your balance. Other mental models encode your knowledge of objects and your interactions with them: knowing how to hold a pen, write, or ride a bike. Others even represent the minds of others: you possess a vast mental catalog of people who are close to you, their appearances, their voices, their tastes, and their quirks.



These mental models can generate hyper-realistic simulations of the universe around us. Did you ever notice that your brain sometimes projects the most authentic virtual reality shows, in which you can walk, move, dance, visit new places, have brilliant conversations, or feel strong emotions? These are your dreams! It is fascinating to realize that all the thoughts that come to us in our dreams, however complex, are simply the product of our free-running internal models of the world.



But we also dream up reality when awake: our brain constantly projects hypotheses and interpretative frameworks on the outside world. This is because, unbeknownst to us, every image that appears on our retina is ambiguous-whenever we see a plate, for instance, the image is compatible with an infinite number of ellipses. If we see the plate as round, even though the raw sense data picture it as an oval, it is because our brain supplies additional data: it has learned that the round shape is the most likely interpretation. Behind the scenes, our sensory areas ceaselessly compute with probabilities, and only the most likely model makes it into our consciousness. It is the brain's projections that ultimately give meaning to the flow of data that reaches us from our senses. In the absence of an internal model, raw sensory inputs would remain meaningless.



Learning allows our brain to grasp a fragment of reality that it had previously missed and to use it to build a new model of the world. It can be a part of external reality, as when we learn history, botany, or the map of a city, but our brain also learns to map the reality internal to our bodies, as when we learn to coordinate our actions and concentrate our thoughts in order to play the violin. In both cases, our brain internalizes a new aspect of reality: it adjusts its circuits to appropriate a domain that it had not mastered before.



Such adjustments, of course, have to be pretty clever. The power of learning lies in its ability to adjust to the external world and to correct for errors-but how does the brain of the learner "know" how to update its internal model when, say, it gets lost in its neighborhood, falls from its bike, loses a game of chess, or misspells the word ecstasy? We will now review seven key ideas that lie at the heart of present-day machine-learning algorithms and that may apply equally well to our brains-seven different definitions of what "learning" means.



Learning Is Adjusting the Parameters

of a Mental Model



Adjusting a mental model is sometimes very simple. How, for example, do we reach out to an object that we see? In the seventeenth century, RenŽ Descartes (1596-1650) had already guessed that our nervous system must contain processing loops that transform visual inputs into muscular commands (see the figure on the next page). You can experience this for yourself: try grabbing an object while wearing somebody else's glasses, preferably someone who is very nearsighted. Even better, if you can, get a hold of prisms that shift your vision a dozen degrees to the left and try to catch the object. You will see that your first attempt is completely off: because of the prisms, your hand reaches to the right of the object that you are aiming for. Gradually, you adjust your movements to the left. Through successive trial and error, your gestures become more and more precise, as your brain learns to correct the offset of your eyes. Now take off the glasses and grab the object: you'll be surprised to see that your hand goes to the wrong location, now way too far to the left!



So, what happened? During this brief learning period, your brain adjusted its internal model of vision. A parameter of this model, one that corresponds to the offset between the visual scene and the orientation of your body, was set to a new value. During this recalibration process, which works by trial and error, what your brain did can be likened to what a hunter does in order to adjust his rifle's viewfinder: he takes a test shot, then uses it to adjust his scope, thus progressively shooting more and more accurately. This type of learning can be very fast: a few trials are enough to correct the gap between vision and action. However, the new parameter setting is not compatible with the old one-hence the systematic error we all make when we remove the prisms and return to normal vision.



Undeniably, this type of learning is a little particular, because it requires the adjustment of only a single parameter (viewing angle). Most of our learning is much more elaborate and requires adjusting tens, hundreds, or even thousands of millions of parameters (every synapse in the relevant brain circuit). The principle, however, is always the same: it boils down to searching, among myriad possible settings of the internal model, for those that best correspond to the state of the external world.



An infant is born in Tokyo. Over the next two or three years, its internal model of language will have to adjust to the characteristics of the Japanese language. This baby's brain is like a machine with millions of settings at each level. Some of these settings, at the auditory level, determine which inventory of consonants and vowels is used in Japanese and the rules that allow them to be combined. A baby born into a Japanese family must discover which phonemes make up Japanese words and where to place the boundaries between those sounds. One of the parameters, for example, concerns the distinction between the sounds /R/ and /L/: this is a crucial contrast in English, but not in Japanese, which makes no distinction between Bill Clinton's election and his erection. . . . Each baby must thus fix a set of parameters that collectively specify which categories of speech sounds are relevant for his or her native language.



A similar learning procedure is duplicated at each level, from sound patterns to vocabulary, grammar, and meaning. The brain is organized as a hierarchy of models of reality, each nested inside the next like Russian dolls-and learning means using the incoming data to set the parameters at every level of this hierarchy. Let's consider a high-level example: the acquisition of grammatical rules. Another key difference which the baby must learn, between Japanese and English, concerns the...

Details zur Ausgabe

• Verlag: Viking
• Erscheinungsdatum: 2020
• Sprache: Englisch
• ISBN 10: 0525559884
• ISBN 13: 9780525559887
• Einband: Gebundene Ausgabe
• Anzahl der Seiten: 352
• Kontakt zum Hersteller: VIKING; https://www.penguin.com/overview-vikingbooks/; ; Attn: Andrea T Sheridan 1745 BROADWAY; New York; NY; 10019; USA
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