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

To learn is to adjust the parameters of an internal model. Learning to aim with one’s finger, for example, consists of setting the offset between vision and action: each aiming error provides useful information that allows one to reduce the gap. In artificial neural networks, although the number of settings is much larger, the logic is the same. Re

For optimal learning, the brain must avoid any distraction.

And, indeed, all things being equal, laughing during learning seems to increase curiosity and enhance subsequent memory.

And all learners benefit from focused attention, active engagement, error feedback, and a cycle of daily rehearsal and nightly consolidation—I call these factors the “four pillars” of learning,

According to this theory, learning is reasoning like a good statistician who chooses, among several alternative theories, that which has the greatest probability of being correct, because it best accounts for the available data.

the more curious you are about something, the more likely you are to remember it. Curiosity even transfers to nearby events: when your curiosity is heightened, you remember incidental details such as the face of a passerby or the person who taught you the information that you were so eager to learn.

At birth, infants possess a rich set of core skills and knowledge. Object concepts, number sense, a knack for languages, knowledge of people and their intentions . . . so many brain modules are already present in young children, and these foundational skills will later be recycled in physics, mathematics, language, and philosophy classes.

while error feedback is essential, many children lose confidence and curiosity because their errors are punished rather than corrected.

Learning, in this sense, therefore means managing an internal hierarchy of rules and trying to infer, as soon as possible, the most general ones that summarize a whole series of observations.