大语言模型与人类认知在多个层面存在深层相似，揭示智能本质的共性。
Cognitive Convergence: Deep Similarities Between Large Language Models and Human Cognition
- 通过五维对比发现模型与人类认知结构高度对应
- 体现推理组织、表征结构、预测学习等核心机制一致性
- 适合关注认知科学与AI融合的研究者阅读
大型语言模型常被视为与人类认知截然不同的异质智能，其表面相似性被归因于拟人化投射。我们主张这一观点有误。尽管在物理基础、学习历史和交互环境等方面存在显著差异，当代基于大模型的系统仍与人类认知在五个维度上展现出显著收敛：推断组织、计算架构、表征结构、以预测为导向的学习，以及支持目标导向行为的类强化学习机制。这些对应关系支持一种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同样适用于现代大语言模型系统。
原文摘要 · Abstract (English)
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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