Understanding how language supports emotion inference remains a topic of debate in emotion science. The present study investigated whether language-derived emotion-concept knowledge would causally support emotion inference by manipulating the language-specific knowledge representations in large language models. Using the prompt technique, 14 attributes of emotion concepts were found to be represented by distinct artificial neuron populations. By manipulating these attribute-related neurons, the majority of the emotion inference tasks showed performance deterioration compared to random manipulations. The attribute-specific performance deterioration was related to the importance of different attributes in human mental space. Our findings provide causal evidence in support of a language-based mechanism for emotion inference and highlight the contributions of emotion-concept knowledge.

翻译：情感科学中，语言如何支持情感推理仍是一个争议性话题。本研究通过操控大语言模型中的语言特异性知识表征，探讨了源自语言的情感概念知识是否因果性地支持情感推理。利用提示技术，我们发现14种情感概念属性由不同的人工神经元群体表征。相较于随机操控，对这些与属性相关的神经元进行操控后，大多数情感推理任务的性能出现下降。属性特异性性能下降与不同属性在人类心理空间中的重要性相关。本研究为基于语言机制的情感推理提供了因果证据，并凸显了情感概念知识的贡献。