Cognitive Thermometers: Machine Learning and Logical Complexity

Abstract:How does the human mind represent semantic categories? Why do natural languages favor certain meanings over others? Prior explanations have relied on logical definability and complexity, but these are highly sensitive to the choice of logical language, rendering some design choices unmotivated. In this article, we propose that machine learning provides a somewhat more agnostic approach to measuring semantic complexity. We review emerging evidence that logic and machine learning often yield converging results on relative complexity and its resulting effects in semantic typology. Where they diverge, learning appears to be a better explanation than logical complexity. We argue that treating machine learning models as ``cognitive thermometers'' enables a unified approach to complexity that bridges symbolic logic and connectionist AI.

Computation and Language (cs.CL)

arXiv:2610.10724 [cs.CL]

(or arXiv:2610.10724v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.10724 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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