How humans infer discrete emotions is a fundamental research question in the field of psychology. While conceptual knowledge about emotions (emotion knowledge) has been suggested to be essential for emotion inference, evidence to date is mostly indirect and inconclusive. As the large language models (LLMs) have been shown to support effective representations of various human conceptual knowledge, the present study further employed artificial neurons in LLMs to investigate the mechanism of human emotion inference. With artificial neurons activated by prompts, the LLM (RoBERTa) demonstrated a similar conceptual structure of 27 discrete emotions as that of human behaviors. Furthermore, the LLM-based conceptual structure revealed a human-like reliance on 14 underlying conceptual attributes of emotions for emotion inference. Most importantly, by manipulating attribute-specific neurons, we found that the corresponding LLM's emotion inference performance deteriorated, and the performance deterioration was correlated to the effectiveness of representations of the conceptual attributes on the human side. Our findings provide direct evidence for the emergence of emotion knowledge representation in large language models and suggest its casual support for discrete emotion inference.

翻译：人类如何推断离散情绪是心理学领域的基础研究问题。尽管关于情绪的概念性知识（情绪知识）被认为对情绪推断至关重要，但迄今为止的证据大多是间接且不确定的。由于大语言模型已被证明能够有效表征人类的各种概念性知识，本研究进一步利用大语言模型中的人工神经元来探究人类情绪推断的机制。通过提示激活人工神经元，大语言模型（RoBERTa）展现出与人类行为相似的27种离散情绪概念结构。此外，基于该大语言模型的概念结构揭示了其对情绪的14种潜在概念属性的依赖方式与人类类似。最重要的是，通过操纵特定属性神经元，我们发现大语言模型相应的情绪推断性能出现下降，且性能下降程度与人类侧概念属性表征的有效性相关。我们的研究为情绪知识表征在大语言模型中的涌现提供了直接证据，并表明其对离散情绪推断具有因果支持作用。