Towards a more flexible Language of Thought: Bayesian grammar updates
  after each concept exposure
  Recent approaches to human concept learning have successfully combined the
power of symbolic, infinitely productive rule systems and statistical learning
to explain our ability to learn new concepts from just a few examples. The aim
of most of these studies is to reveal the underlying language structuring these
representations and providing a general substrate for thought. However,
describing a model of thought that is fixed once trained is against the
extensive literature that shows how experience shapes concept learning. Here,
we ask about the plasticity of these symbolic descriptive languages. We perform
a concept learning experiment that demonstrates 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
difficulties to pin it down empirically.
