对比大模型与人脑对句子的理解机制，发现高阶语义越接近。
Do Large Language Models Think Like the Brain? Sentence-Level Evidences from Layer-Wise Embeddings and fMRI
- 用分层嵌入对比人类听故事时的fMRI脑活动
- 模型越强，其高层表征越接近人脑语义区激活模式
- 适合关注AI与脑科学交叉的研究者
理解大语言模型（LLMs）与人脑是否在计算原理上趋同，是认知神经科学与人工智能的核心问题。本研究聚焦句子级语言处理机制，系统考察14个公开大模型的分层表示与人类在自然叙事故事中的动态脑响应之间的对应关系。通过将模型分层嵌入与被试的fMRI数据进行对比，构建句子级神经预测模型，识别出与特定脑区激活最显著相关的模型层。结果显示，模型性能提升推动表征架构向类脑层次结构演进，在高层语义抽象层面实现更强的功能与解剖对应。该研究深化了对大模型与人脑计算平行性的认识，凸显了大模型作为人类语言处理建模工具的潜力。
原文摘要 · Abstract (English)
Understanding whether large language models (LLMs) and the human brain converge on similar computational principles remains a fundamental and important question in cognitive neuroscience and AI. Do the brain-like patterns observed in LLMs emerge simply from scaling, or do they reflect deeper alignment with the architecture of human language processing? This study focuses on the sentence-level neural mechanisms of language models, systematically investigating how layer-wise representations in LLMs align with the dynamic neural responses during human sentence comprehension. By comparing hierarchical embeddings from 14 publicly available LLMs with fMRI data collected from participants, who were exposed to a naturalistic narrative story, we constructed sentence-level neural prediction models to identify the model layers most significantly correlated with brain region activations. Results show that improvements in model performance drive the evolution of representational architectures toward brain-like hierarchies, particularly achieving stronger functional and anatomical correspondence at higher semantic abstraction levels. These findings advance our understanding of the computational parallels between LLMs and the human brain, highlighting the potential of LLMs as models for human language processing.
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