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This paper challenges the notion that large language models (LLMs) operate on fundamentally different cognitive principles than humans, revealing significant structural correspondences across five dimensions: inferential organization, computational architecture, representational structure, prediction-driven learning, and reinforcement-learning-like mechanisms. By highlighting these similarities, the authors argue for a reevaluation of LLMs as systems that, despite their distinct physical substrates and learning histories, share core cognitive principles with human intelligence. This convergence suggests a unified framework for understanding intelligence that encompasses both human cognition and LLM operations, with implications for future AI development and cognitive science research.
LLMs and human cognition share strikingly similar principles of cognitive organization, challenging the view of AI as an alien intelligence.
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.
