大型语言模型与人类大脑在创造性思维中实现同频共振

Large Language Models Align with the Human Brain during Creative Thinking

April 3, 2026

作者: Mete Ismayilzada, Simone A. Luchini, Abdulkadir Gokce, Badr AlKhamissi, Antoine Bosselut, Antonio Laverghetta Jr., Lonneke van der Plas, Roger E. Beaty

cs.AI

摘要

创造性思维是人类认知的基本特征，而作为其核心生成引擎的发散性思维——即产生新颖多元想法的能力——已获得广泛认同。大型语言模型（LLM）近期在发散性思维测试中展现出卓越表现，先前研究亦表明任务性能越高的模型与人类大脑活动越趋一致。然而现有脑-LML对齐研究多聚焦于被动型非创造性任务。本研究利用170名参与者执行替代用途任务（AUT）时的功能磁共振成像数据，探索创造性思维过程中的脑模型对齐机制。我们提取了不同规模（2.7亿-720亿参数）LLM的表征，通过表征相似性分析（RSA）测量其与大脑反应的对齐度，重点关注与创造力相关的默认模式网络和额顶网络。研究发现：脑-LML对齐度随模型规模（仅默认模式网络）和想法原创性（双网络）提升而增强，且在创造性过程初期效应最为显著。我们进一步揭示训练后目标会以功能选择性方式塑造对齐模式：经过创造力优化的Llama-3.1-8B-Instruct模型能保持与高创造力神经响应的对齐，同时降低与低创造力响应的关联；经人类行为微调的模型则提升与两者的对齐度；而推理训练变体呈现相反模式，表明思维链训练会使表征偏离创造性神经几何结构转向分析性处理。这些结果证实，训练后目标能针对人类创造性思维的神经几何特征，对LLM表征进行选择性重塑。

English

Creative thinking is a fundamental aspect of human cognition, and divergent thinking-the capacity to generate novel and varied ideas-is widely regarded as its core generative engine. Large language models (LLMs) have recently demonstrated impressive performance on divergent thinking tests and prior work has shown that models with higher task performance tend to be more aligned to human brain activity. However, existing brain-LLM alignment studies have focused on passive, non-creative tasks. Here, we explore brain alignment during creative thinking using fMRI data from 170 participants performing the Alternate Uses Task (AUT). We extract representations from LLMs varying in size (270M-72B) and measure alignment to brain responses via Representational Similarity Analysis (RSA), targeting the creativity-related default mode and frontoparietal networks. We find that brain-LLM alignment scales with model size (default mode network only) and idea originality (both networks), with effects strongest early in the creative process. We further show that post-training objectives shape alignment in functionally selective ways: a creativity-optimized Llama-3.1-8B-Instruct preserves alignment with high-creativity neural responses while reducing alignment with low-creativity ones; a human behavior fine-tuned model elevates alignment with both; and a reasoning-trained variant shows the opposite pattern, suggesting chain-of-thought training steers representations away from creative neural geometry toward analytical processing. These results demonstrate that post-training objectives selectively reshape LLM representations relative to the neural geometry of human creative thought.