ArticlePloS one2018
Bayesian validation of grammar productions for the language of thought.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Verbal interference paradigms: A systematic review investigating the role of language in cognition.Psychonomic bulletin & review · 2023Pooled it
- The Role of Mindfulness Therapy in the Treatment of Chronic Pain.Current pain and headache reports · 2024Review
- The cultural origins of symbolic number.Psychological review · 2022Article
- One model for the learning of language.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- The computational origin of representation.Minds and machines · 2021Article
- A theory of memory for binary sequences: Evidence for a mental compression algorithm in humans.PLoS computational biology · 2021Article
- The Child as Hacker.Trends in cognitive sciences · 2020Review
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6 authors.
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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 propose an extra validation step for the set of atomic productions defined by the experimenter. It starts by expanding the defined LoT grammar for the cognitive domain with a broader set of arbitrary productions and then uses Bayesian inference to prune the productions from the experimental data. The result allows the researcher to validate that the resulting grammar still matches the intuitive grammar chosen for the domain. We then test this method in the language of geometry, a specific LoT model for geometrical sequence learning. Finally, despite the fact of the geometrical LoT not being a universal (i.e. Turing-complete) language, we show an empirical relation between a sequence's probability and its complexity consistent with the theoretical relationship for universal languages described by Levin's Coding Theorem.
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