2018/05/17 by Pablo Tano, Sergio Romano, Mariano Sigman +2 · 1 voice
Computer Science · #Advanced Text Analysis Techniques #Evolutionary Algorithms and Applications #Neural Networks and Applications #cs.AI
paper · pdf · doi:10.1103/physreve.101.042128
published as Phys. Rev. E 101, 042128 (2020)
arxiv published 2018/05/17 · arxiv created 2019/09/26 · openalex publication_date 2020/04/23 · arxiv updated 2020/04/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.