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

2026-07-28

AI Summary

This study challenges the prevailing view that large language models (LLMs) are fundamentally heterogeneous to human cognition, systematically investigating whether deep convergences exist at the level of cognitive mechanisms. Drawing on cognitive science theory and employing computational modeling alongside interdisciplinary comparative methods, the work reveals a striking alignment between LLMs and human cognition across five dimensions: reasoning organization, computational architecture, representational structure, predictive learning, and reinforcement-like learning. The research proposes a unified cognitive framework for intelligence, offering a novel paradigm for understanding the commonalities between artificial and human intelligence. It demonstrates that LLMs exhibit not merely superficial anthropomorphism but structural similarities with human cognition at the core mechanistic level.

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.

Problem

• large language models
• human cognition
• cognitive convergence
• cognitive organization
• artificial intelligence

Innovation

• cognitive convergence
• large language models
• human cognition
• predictive learning
• reinforcement learning