Emotion significantly impacts our daily behaviors and interactions. While recent generative AI models, such as large language models, have shown impressive performance in various tasks, it remains unclear whether they truly comprehend emotions. This paper aims to address this gap by incorporating psychological theories to gain a holistic understanding of emotions in generative AI models. Specifically, we propose three approaches: 1) EmotionPrompt to enhance AI model performance, 2) EmotionAttack to impair AI model performance, and 3) EmotionDecode to explain the effects of emotional stimuli, both benign and malignant. Through extensive experiments involving language and multi-modal models on semantic understanding, logical reasoning, and generation tasks, we demonstrate that both textual and visual EmotionPrompt can boost the performance of AI models while EmotionAttack can hinder it. Additionally, EmotionDecode reveals that AI models can comprehend emotional stimuli akin to the mechanism of dopamine in the human brain. Our work heralds a novel avenue for exploring psychology to enhance our understanding of generative AI models.

翻译：情绪显著影响我们的日常行为与互动。尽管近期生成式人工智能模型（如大语言模型）已在多项任务中展现出卓越性能，但其是否真正理解情绪仍不明确。本文通过融合心理学理论，旨在系统探究生成式人工智能模型中的情绪机制。具体而言，我们提出三种方法：1) 提升AI模型性能的EmotionPrompt；2) 削弱AI模型性能的EmotionAttack；3) 解析良性与恶性情绪刺激影响的EmotionDecode。通过在语义理解、逻辑推理与生成任务中对语言及多模态模型开展大量实验，我们证明文本与视觉形式的EmotionPrompt均能提升AI模型性能，而EmotionAttack则会对其产生抑制。此外，EmotionDecode揭示AI模型理解情绪刺激的机制类似于人脑多巴胺的作用原理。本研究为借助心理学探索生成式人工智能模型开辟了新路径。