Decoding Neural Emotion Patterns through Large Language Model Embeddings

TL;DR

This study introduces a scalable computational framework that links textual emotional content to brain regions using language model embeddings, enabling analysis of natural language and clinical differences without neuroimaging.

Contribution

The paper presents a novel method for mapping text-derived emotional representations to brain regions, bridging computational linguistics and neuroscience without neuroimaging data.

Findings

Neuroanatomically plausible emotion mappings with high spatial specificity.

Depressed subjects show greater limbic engagement linked to negative affect.

LLM-generated text matches human emotion distribution but lacks nuanced brain activation.

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.