用文本嵌入映射大脑情绪区域，无需脑成像即可分析情绪与脑区关系。
Decoding Neural Emotion Patterns through Large Language Model Embeddings
- 用LLM文本嵌入生成语义表示，通过降维聚类定位情绪相关脑区。
- 抑郁人群在边缘系统激活更强，与负性情绪关联显著。
- 可区分人类与大模型文本的情绪脑激活差异，适合心理研究与AI评估。
理解语言情绪表达与脑功能的关系是计算神经科学与情感计算中的挑战。传统神经影像成本高且受限于实验室，而海量数字文本为情绪-脑映射提供了新路径。以往研究多孤立进行神经影像情绪定位或文本计算分析，缺乏整合。本文提出一种无需神经影像的计算框架，将文本情绪内容映射至解剖定义的大脑区域。基于OpenAI的text-embedding-ada-002生成高维语义表示，经降维与聚类识别情绪群组，并映射至18个情绪处理相关脑区。三个实验包括：一）对比健康与抑郁个体（DIAC-WOZ数据集）的对话数据映射模式；二）应用于GoEmotions数据集；三）比较人类文本与大语言模型生成文本的推断脑激活差异。情绪强度通过词法分析评分。结果表明映射具有高度空间特异性，符合神经解剖学规律。抑郁者在边缘系统有更强激活，与负性情绪相关。离散情绪得以有效区分。大模型生成文本在基本情绪分布上与人类相似，但在共情及自我参照区域（内侧前额叶皮层与后扣带皮层）的激活更弱。该方法成本低、可扩展，适用于自然语言的大规模分析，能区分临床群体，并为评估AI情绪表达提供脑基基准。
原文摘要 · Abstract (English)
Understanding how emotional expression in language relates to brain function is a challenge in computational neuroscience and affective computing. Traditional neuroimaging is costly and lab-bound, but abundant digital text offers new avenues for emotion-brain mapping. Prior work has largely examined neuroimaging-based emotion localization or computational text analysis separately, with little integration. We propose a computational framework that maps textual emotional content to anatomically defined brain regions without requiring neuroimaging. Using OpenAI's text-embedding-ada-002, we generate high-dimensional semantic representations, apply dimensionality reduction and clustering to identify emotional groups, and map them to 18 brain regions linked to emotional processing. Three experiments were conducted: i) analyzing conversational data from healthy vs. depressed subjects (DIAC-WOZ dataset) to compare mapping patterns, ii) applying the method to the GoEmotions dataset and iii) comparing human-written text with large language model (LLM) responses to assess differences in inferred brain activation. Emotional intensity was scored via lexical analysis. Results showed neuroanatomically plausible mappings with high spatial specificity. Depressed subjects exhibited greater limbic engagement tied to negative affect. Discrete emotions were successfully differentiated. LLM-generated text matched humans in basic emotion distribution but lacked nuanced activation in empathy and self-referential regions (medial prefrontal and posterior cingulate cortex). This cost-effective, scalable approach enables large-scale analysis of naturalistic language, distinguishes between clinical populations, and offers a brain-based benchmark for evaluating AI emotional expression.
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