Humans no doubt use language to communicate about their emotional experiences, but does language in turn help humans understand emotions, or is language just a vehicle of communication? This study used a form of artificial intelligence (AI) known as large language models (LLMs) to assess whether language-based representations of emotion causally contribute to the AI's ability to generate inferences about the emotional meaning of novel situations. Fourteen attributes of human emotion concept representation were found to be represented by the LLM's distinct artificial neuron populations. By manipulating these attribute-related neurons, we in turn demonstrated the role of emotion concept knowledge in generative emotion inference. The attribute-specific performance deterioration was related to the importance of different attributes in human mental space. Our findings provide a proof-in-concept that even a LLM can learn about emotions in the absence of sensory-motor representations and highlight the contribution of language-derived emotion-concept knowledge for emotion inference.

翻译：人类无疑使用语言来交流情绪体验，但语言是否反过来帮助人类理解情绪，抑或仅仅是沟通的媒介？本研究采用一种名为大语言模型的人工智能形式，评估基于语言的情绪表征是否因果性地促进了该模型对新颖情境中情绪含义的推理能力。研究发现，人类情绪概念表征的十四个属性能够由大语言模型的不同人工神经元群体所表征。通过操控这些与属性相关的神经元，我们进而证明了情绪概念知识在生成式情绪推理中的作用。特定属性的表现衰退与不同属性在人类心理空间中的重要性相关。我们的研究结果提供了一个概念验证：即使是大语言模型也能在缺乏感觉-运动表征的情况下学习情绪，并凸显了语言衍生情绪概念知识对情绪推理的贡献。