发表机构
University of Michigan; Weinberg Institute for Cognitive Science（密歇根大学; 温伯格认知科学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究指出，尽管大语言模型与人类存在多方面差异，但在推理组织等五个认知维度上与人类认知结构趋同，相关核心原则可用于解释基于大语言模型的系统智能。
AI 中文摘要
大语言模型（LLMs）被广泛视为异质智能体，其认知运作与人类存在根本差异，因此其与人类认知的表面相似常被归为拟人化投射。我们认为该观点有误：LLMs虽在物理载体、学习历史、交互环境等重要方面与人类不同，但当代基于LLM的系统仍在认知组织的多项原则上与人类认知趋同，这些原则在认知科学中已获长期支持。我们在五个维度识别出结构对应：推理组织、计算架构、表征结构、预测驱动学习，以及支持目标导向行动的类强化学习机制。这些对应支撑了更广泛的智能认知模型，即长期用于解释人类智能的核心原则也适用于当代基于LLM的系统。
英文摘要
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.
Comments23 pages, 0 figures
