Do Large Language Models Have “Emotion Neurons”? Investigating the Existence and Role (2025)

Abstract: This study comprehensively explores whether there actually exist “emotion neurons” within large language models (LLMs) that selectively process and express certain emotions, and what functional role they play. Drawing on the representative emotion theory of the six basic emotions, we focus on six core emotions. Using synthetic dialogue data labeled with emotions, we identified sets of neurons that exhibit consistent activation patterns for each emotion. As a result, we confirmed that principal neurons handling emotion information do indeed exist within the model, forming distinct groups for each emotion, and that their distribution varies with model size and architectural depth. We then validated the functional significance of these emotion neurons by analyzing whether the prediction accuracy for a specific emotion significantly decreases when those neurons are artificially removed. We observed that in some emotions, the accuracy drops sharply upon neuron removal, while in others, the model’s performance largely remains intact or even improves, presumably due to overlapping and complementary mechanisms among neurons. Furthermore, by examining how prediction accuracy changes depending on which layer range and at what proportion the emotion neurons are masked, we revealed that emotion information is processed in a multilayered and complex manner within the model.

## introduction emotions play a crucial role in human cognition, behavior, and communication @xcite . as large language models (llms) are increasingly utilized in various application domains, the ability to understand and appropriately respond to human emotions is becoming ever more important. conventional methods have primarily assessed llms' emotional capabilities using metrics such as classification accuracy in emotion classification tasks @xcite or response generation that reflects specific emotional states @xcite . however, these outcome-based evaluations have been criticized for not directly illuminating how emotion information is processed and represented within an llm. against this backdrop, the present study systematically investigates both the existence and functional role of neurons (hereafter referred to as "emotion neurons") within llms that selectively process and represent specific emotions. this concept extends the "sentiment neuron" proposed by @xcite , examining whether largescale models contain sets of neurons that are actually highly responsive to certain emotions and, if so, how these neurons function in terms of overlap, distribution across layers, and so on. in particular, this study focuses on the conversational context, where emotion processing is critically important in human-ai interaction, and adopts the six basic emotions (happiness, sadness, anger, disgust, fear, surprise) as proposed by @xcite . to this end, we leveraged representative llms and a synthetic dialogue dataset containing explicit emotion expressions to explore three key research questions (rq1, rq2, rq3) in a stepby-step manner. first, rq1 asks, "do emotion neuron groups actually exist within llms that are responsible for particular emotions?" to address this, we conducted a detailed analysis of neuron activation patterns in each layer of the model, identifying neuron groups that consistently activate under specific emotions. next, rq2 examines whether these "emotion neurons," once identified in rq1, truly serve a functional role by observing how the model's emotion prediction performance changes when they are manipulated. specifically, we investigated how overlaps among emotion neurons and complementary relationships between different emotions influence performance variations. finally, in rq3, we aimed to refine our understanding of where emotion neurons are distributed within the model and how they integrate emotion information by examining, from multiple angles, how manipulating emotion neurons at different layer ranges and at different ratios affects the model's emotion prediction accuracy. rather than simply treating the entire set of emotion neurons as "present/absent," this involved exploring changes in the model's emotion processing that arise from varying both the intensity and the layer-based position of neuron control, thus offering a deeper understanding of how llms internally represent emotions. our experimental results confirmed that a significant number of neurons related to specific emotions exist within llms, and that intentionally manipulating these neurons leads to a marked decline in performance for those emotions. moreover, the distribution of these neurons varies depending on the scale of the model and the depth of its architecture, and we observed strong overlaps in certain emotions, indicating complementary effects among the emotion neurons. finally, the analysis focusing on particular layer ranges and manipulation ratios showed that the location and intensity of these emotion neurons have a decisive impact on the model's emotion prediction capabilities. ## related work with recent rapid advances in llms, there has been a notable improvement in performance across a wide range of natural language processing tasks @xcite . these developments have demonstrated near human-level language understanding and generation capabilities across diverse application domains, prompting researchers to probe the limitations of llms more deeply @xcite . in particular, questions have arisen about whether llms can respond in ways analogous to human emotional states and to what extent they possess human-like emotional understanding. this has driven active research into llms in the context of human-ai interaction @xcite . ## methodology this section outlines the detailed procedure used to identify emotion neurons within an llm and analyze their functionality. we first provide an overview of the model architecture that grants access to emotion neurons, followed by a description of the core techniques for detecting and selecting such neurons. all methodological steps described here were applied to the dataset introduced in appendix b. ## results in this section, we investigate whether emotion neurons within an llm truly exist to handle particular emotions. we conducted an emotion-wise neuron distribution analysis on two models, llama-3.1-8b-instruct and llama-3.1-70b-instruct. if emotion neurons do exist, we should be able to identify neuron groups in the model that display consistent activation patterns linked to specific emotions (e.g., anger, disgust, fear, happiness, sadness, surprise). to verify this, we quantitatively measured the extent to which activation is concentrated on each emotion per layer and then visualized the distribution across layers in figure 1 . existence of emotion neurons and differences in neuron distribution for each emotion. our analysis showed that neurons with a strong correlation to particular emotions exist in both models above a certain threshold. in llama-3.1-8b-instruct, the emotion with the largest total number of identified neurons was anger (1,882 neurons), followed by fear (1,629), disgust (1,598), sadness (1,570), happiness (1,320), and surprise (1,070). this indicates that neuron groups for particular emotional categories are distinct and suggests a mechanism of emotional representation in llms akin to the "sentiment neuron" proposed by @xcite . in llama-3.1-70b-instruct, the absolute number of emotion neurons increased overall, yet surprise (4,976) remained the smallest distribution. interestingly, in the 70b model, sadness (8,314) surpassed anger (7,910) as the emotion with the most neurons, confirming that the relative distribution can shift as the model size grows. this suggests that both training procedures and the growth in parameters can influence the model's internal emotional representation structure. layer-wise distribution patterns of emotion neurons. examining how emotion neurons are distributed across layers revealed that both models exhibit a "distribution curve" in neuron activation, rather than a uniform pattern throughout the layers. ## conclusion this study systematically explored whether neurons exist within llms that process specific emotions (rq1), how the model's emotion recognition function changes when these neurons are selectively removed (rq2), and how emotion neurons are distributed across layers, as well as how changing masking ratios and layers affects prediction accuracy (rq3). the experimental findings revealed significant sets of neurons for major emotions, with certain emotions showing a dramatic decrease in prediction accuracy when their neurons were removed, indicating these neurons serve a functionally important role. at the same time, we observed that overlapping and substitutive mechanisms operate among emotion neurons, so that for some emotions, model performance did not significantly drop or even improved, demonstrating that emotional representation is organized by mutually complementary structures rather than a simple oneto-one mapping. moreover, stepwise masking or selectively manipulating certain layer ranges resulted in varying effects on emotion prediction, suggesting a complex, multi-layer mechanism in which emotional information is dispersed, integrated, and processed.