Sublime

active engagement. Converging results from diverse fields suggest that a passive organism learns little or nothing. Efficient learning means refusing passivity, engaging, exploring, and actively generating hypotheses and testing them on the outside world.

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.

In this view, learning becomes similar to programming: it consists of selecting the simplest internal formula that fits the data, among all those available in the language of thought.

Think of learning as your brain getting an upgrade. When you pay attention to knowledge or information and it makes sense to you, this interaction with the environment leaves biological impressions in your brain.

in the vast majority of cases, and as soon as learning concerns high-level cognitive properties, such as the explicit memory of word meanings rather than their mere form, learning seems to occur only if the learner pays attention, thinks, anticipates, and puts forth hypotheses at the risk of making mistakes. Without attention, effort, and in-depth

Learning means trying to select the simplest model that fits the data. Suppose I show you the top card and tell you that the three objects surrounded by thick lines are “tufas.” With so little data, how do you find the other tufas? Your brain makes a model of how these forms were generated, a hierarchical tree of their properties, and then selects

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.

Learning requires two structures: an immense set of potential models and an efficient algorithm to adjust them to reality.