Understanding emotions is key to Affective Computing. Emotion recognition focuses on the communicative component of emotions encoded in social signals. This view alone is insufficient for a deeper understanding and computational representation of the internal, subjectively experienced component of emotions. This article presents a cognition-based method called Deep as a starting point for deeper computational modeling of the internal component of emotions. Deep incorporates an approach to query individual internal emotional experiences and to represent such information computationally. It combines social signals, verbalized introspection information, context information, and theory-driven knowledge. We apply the Deep method to the emotion of shame as an example and compare it to a typical emotion recognition model, highlighting the differences and advantages.

中文翻译：

---

深层方法：理论、内省和社交信号驱动的社会情感羞耻的计算模型


理解情绪是情感计算的关键。情绪识别侧重于社交信号中编码的情绪的交流成分。仅此观点不足以更深入地理解和计算表示情感的内部、主观体验的组成部分。本文提出了一种称为 Deep 的基于认知的方法，作为对情感内部成分进行更深入的计算建模的起点。 Deep 采用了一种查询个人内部情感体验并通过计算表示此类信息的方法。它结合了社会信号、语言化的内省信息、情境信息和理论驱动的知识。我们将 Deep 方法应用于羞耻情绪为例，并将其与典型的情绪识别模型进行比较，突出了差异和优势。