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. 2023 Sep 28:46:e271.
doi: 10.1017/S0140525X23002029.

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

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

Matteo Colombo. Behav Brain Sci. .

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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