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Understanding how language supports emotion inference remains a topic ofdebate in emotion science. The present study investigated whetherlanguage-derived emotion-concept knowledge would causally support emotioninference by manipulating the language-specific knowledge representations inlarge language models. Using the prompt technique, 14 attributes of emotionconcepts were found to be represented by distinct artificial neuronpopulations. By manipulating these attribute-related neurons, the majority ofthe emotion inference tasks showed performance deterioration compared to randommanipulations. The attribute-specific performance deterioration was related tothe importance of different attributes in human mental space. Our findingsprovide causal evidence in support of a language-based mechanism for emotioninference and highlight the contributions of emotion-concept knowledge.

Language-Specific Representation of Emotion-Concept Knowledge Causally Supports Emotion Inference