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Cognitive Convergence: Deep Similarities Between LLMs and Human Cognition (2026)

A paper posted on arXiv on July 28, 2026, argues that large language models (LLMs) converge with human cognition across five dimensions: inferential organization, computational architecture, representational structure, prediction-driven learning, and reinforcement-learning-like mechanisms. The authors contend that apparent similarities are not merely anthropomorphic projection, despite differences in physical substrate, learning history, and environments.

read2 min views1 publishedAug 17, 2026
Cognitive Convergence: Deep Similarities Between LLMs and Human Cognition (2026)
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[Submitted on 28 Jul 2026]


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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.

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