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Concept learning in a probabilistic language-of-thought. How is it possible and what does it presuppose?

Published online by Cambridge University Press:  28 September 2023

Matteo Colombo Open the ORCID record for Matteo Colombo [Opens in a new window]
Matteo Colombo*
Affiliation:
Tilburg Center for Logic and Philosophy of Science (TiLPS), Tilburg University, Tilburg, The Netherlands m.colombo@uvt.nl; https://mteocolphi.wordpress.com/
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Abstract

Where does a probabilistic language-of-thought (PLoT) come from? How can we learn new concepts based on probabilistic inferences operating on a PLoT? Here, I explore these questions, sketching a traditional circularity objection to LoT and canvassing various approaches to addressing it. I conclude that PLoT-based cognitive architectures can support genuine concept learning; but, currently, it is unclear that they enjoy more explanatory breadth in relation to concept learning than alternative architectures that do not posit any LoT.

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Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 46 , 2023 , e271
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Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press

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