Do Large Language Models Think Like the Brain? Sentence-Level Evidence from fMRI and Hierarchical Embeddings

• URL: http://arxiv.org/abs/2505.22563v1
• Date: Wed, 28 May 2025 16:40:06 GMT
• Title: Do Large Language Models Think Like the Brain? Sentence-Level Evidence from fMRI and Hierarchical Embeddings
• Authors: Yu Lei, Xingyang Ge, Yi Zhang, Yiming Yang, Bolei Ma,
• Abstract summary: This study investigates how hierarchical representations in large language models align with the dynamic neural responses during human sentence comprehension.<n>Results show that improvements in model performance drive the evolution of representational architectures toward brain-like hierarchies.
• Score: 28.210559128941593
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 hierarchical 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 precisely 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.

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