标题： 通过自然语言处理嵌入解码神经情感模式 显示英文标题

标题： Decoding Neural Emotion Patterns through Natural Language Processing Embeddings

摘要： 理解语言中的情感表达如何与脑功能相关是计算神经科学和情感计算中的一个挑战。传统的神经成像成本高昂且局限于实验室，但丰富的数字文本为情感-脑映射提供了新的途径。先前的研究主要分别探讨基于神经成像的情感定位或计算文本分析，很少进行整合。我们提出了一种计算框架，能够在不依赖神经成像的情况下将文本情感内容映射到解剖定义的脑区。使用OpenAI的text-embedding-ada-002，我们生成高维语义表示，应用降维和聚类来识别情感组，并将它们映射到与情感处理相关的18个脑区。进行了三项实验：i) 分析健康与抑郁受试者的对话数据（DIAC-WOZ数据集）以比较映射模式，ii) 将该方法应用于GoEmotions数据集，iii) 比较人工撰写的文本与大型语言模型（LLM）的回答，以评估推断的脑激活差异。情感强度通过词汇分析进行评分。结果显示出具有高空间特异性的神经解剖学合理的映射。抑郁受试者表现出与负面情绪相关的边缘系统参与度更高。离散情绪成功区分。LLM生成的文本在基本情绪分布上与人类相当，但在共情和自我参照区域（内侧前额叶皮层和后扣带回皮层）缺乏细微的激活。这种成本效益高、可扩展的方法使得对自然语言的大规模分析成为可能，能够区分不同临床群体，并为评估人工智能情感表达提供基于脑的基准。 显示英文摘要

摘要： 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.