Learning is Compiling: Experience Shapes Concept Learning by Combining Primitives in a Language of Thought
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Recent approaches to human concept learning have successfully combined the power of symbolic, infinitely productive, rule systems and statistical learning. The aim of most of these studies is to reveal the underlying language structuring these representations and providing a general substrate for thought. Here, we ask about the plasticity of symbolic descriptive languages. We perform two concept learning experiments, that consistently demonstrate that humans can change very rapidly the repertoire of symbols they use to identify concepts, by compiling expressions which are frequently used into new symbols of the language. The pattern of concept learning times is accurately described by a Bayesian agent that rationally updates the probability of compiling a new expression according to how useful it has been to compress concepts so far. By portraying the Language of Thought as a flexible system of rules, we also highlight the intrinsic difficulties to pin it down empirically.
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