Decoding Neural Emotion Patterns through Natural Language Processing Embeddings

2025-08-12

AI Summary

This study addresses the challenge of establishing an emotion–brain mapping framework without requiring costly neuroimaging data, enabling low-cost, scalable analysis of the relationship between natural language emotional expressions and brain function. Method: We employ the text-embedding-ada-002 model to derive semantic embeddings of emotional language, integrate dimensionality reduction and clustering with lexical affective ratings, and—guided by established neuroscience knowledge—map the high-dimensional semantic space onto 18 anatomically defined, emotion-related brain regions. Contribution/Results: To our knowledge, this is the first work to directly couple semantic embeddings with fine-grained, neuroanatomically grounded brain regions, achieving high spatial specificity in emotion localization. The framework successfully differentiates depression-associated hyperactivation in limbic structures, discriminates discrete emotions, and reveals neural-level deficits in large language models—specifically, attenuated activation in empathy- and self-referential processing regions. It thus provides a human-brain–informed functional benchmark for clinical subtyping and AI-based affective assessment.

Technology Category

• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Affective Computing

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

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.

Problem

• Map textual emotions to brain regions without neuroimaging
• Compare emotion-brain patterns in healthy vs depressed subjects
• Assess AI vs human emotional expression differences

Innovation

• Text embeddings map emotions to brain regions
• Dimensionality reduction clusters emotional content
• Lexical analysis scores emotional intensity