[2607.26179] Cognitive Convergence: Deep Similarities Between Large Language Models and Human Cognition

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Quantitative Biology > Neurons and Cognition

arXiv:2607.26179 (q-bio)

[Submitted on 28 Jul 2026]

Title:Cognitive Convergence: Deep Similarities Between Large Language Models and Human Cognition

Authors:Chandra Sripada, Richard Lewis<br>View a PDF of the paper titled Cognitive Convergence: Deep Similarities Between Large Language Models and Human Cognition, by Chandra Sripada and 1 other authors

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Abstract:LLMs are widely regarded as alien intelligences, systems whose cognitive operations are fundamentally unlike our own. Apparent similarities to human cognition are therefore often seen as the result of anthropomorphic projection. We argue that this framing is mistaken. LLMs clearly differ from humans in important respects, including their physical substrate, learning history, and the environments with which they interact. These differences make it all the more striking that contemporary LLM-based systems converge with human cognition on a number of principles of cognitive organization with longstanding support in cognitive science. We identify structural correspondences across five dimensions: inferential organization, computational architecture, representational structure, prediction-driven learning, and reinforcement-learning-like mechanisms supporting goal-directed action. These correspondences support a broader model of intelligent cognition in which core principles long used to explain human intelligence also characterize contemporary LLM-based systems.

Comments:<br>23 pages, 0 figures

Subjects:

Neurons and Cognition (q-bio.NC); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

Cite as:<br>arXiv:2607.26179 [q-bio.NC]

(or<br>arXiv:2607.26179v1 [q-bio.NC] for this version)

https://doi.org/10.48550/arXiv.2607.26179

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arXiv-issued DOI via DataCite

Submission history<br>From: Chandra Sripada [view email]<br>[v1]<br>Tue, 28 Jul 2026 18:37:48 UTC (260 KB)

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