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bioRxiv · 10.1101/141358

Bayesian Selection Of Grammar Productions For The Language Of Thought

Romano, S.·Salles, A.·Amalric, M.·Dehaene, S.·Sigman, M.·Figueria, S.
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Abstract

Probabilistic proposals of Language of Thoughts (LoTs) can explain learning across different domains as statistical inference over a compositionally structured hypothesis space. While frameworks may differ on how a LoT may be implemented computationally, they all share the property that they are built from a set of atomic symbols and rules by which these symbols can be combined. In this work we show how the set of productions of a LoT grammar can be effectively selected from a broad repertoire of possible productions by an inferential process starting from experimental data. We then test this method in the language of geometry, a specific LoT model (Amalric et al., 2017). Finally, despite the fact of the geometrical LoT not being a universal (i.e. Turing-complete) language, we show an empirical relation between a sequences probability and its complexity consistent with the theoretical relationship for universal languages described by Levins Coding Theorem.

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Romano, S., Salles, A., Amalric, M., Dehaene, S., Sigman, M., Figueria, S.. 2017-05-24. Bayesian Selection Of Grammar Productions For The Language Of Thought. https://doi.org/10.1101/141358

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