As large language models (LLMs) increasingly power conversational agents, understanding how they model users' emotional states is critical for ethical deployment. Inspired by emotion wheels, i.e., a psychological framework that argues emotions organize hierarchically, we analyze probabilistic dependencies between emotional states in model outputs. We find that LLMs naturally form hierarchical emotion trees that align with human psychological models, and larger models develop more complex hierarchies. We also uncover systematic biases in emotion recognition across socioeconomic personas, with compounding misclassifications for intersectional, underrepresented groups. Human studies reveal striking parallels, suggesting that LLMs internalize aspects of social perception. Beyond highlighting emergent emotional reasoning in LLMs, our results hint at the potential of using cognitively-grounded theories for developing better model evaluations.

翻译：随着大型语言模型（LLMs）日益成为对话智能体的核心驱动力，理解它们如何建模用户情感状态对伦理部署至关重要。受情感轮（一种认为情感呈层级化组织的心理学框架）启发，我们分析了模型输出中情感状态之间的概率依赖关系。研究发现，LLMs会自然形成与人类心理学模型相一致的层级化情感树，且更大规模的模型会发展出更复杂的层级结构。我们还揭示了模型在社会经济身份感知中存在系统性情感识别偏差，尤其对交叉性弱势群体表现出叠加性的误分类倾向。人类行为研究显示了显著的相似性，表明LLMs内化了社会知觉的某些方面。这项研究不仅揭示了LLMs中涌现的情感推理能力，更提示了利用认知心理学理论开发更优模型评估方法的潜力。