Emotion understanding is a complex process that involves multiple components.
The ability to recognise emotions not only leads to new context awareness
methods but also enhances system interaction's effectiveness by perceiving and
expressing emotions. Despite the attention to discrete and dimensional models,
neuroscientific evidence supports those emotions as being complex and
multi-faceted. One framework that resonated well with such findings is the
Component Process Model (CPM), a theory that considers the complexity of
emotions with five interconnected components: appraisal, expression,
motivation, physiology and feeling. However, the relationship between CPM and
discrete emotions has not yet been fully explored. Therefore, to better
understand emotions underlying processes, we operationalised a data-driven
approach using interactive Virtual Reality (VR) games and collected multimodal
measures (self-reports, physiological and facial signals) from 39 participants.
We used Machine Learning (ML) methods to identify the unique contributions of
each component to emotion differentiation. Our results showed the role of
different components in emotion differentiation, with the model including all
components demonstrating the most significant contribution. Moreover, we found
that at least five dimensions are needed to represent the variation of emotions
in our dataset. These findings also have implications for using VR environments
in emotion research and highlight the role of physiological signals in emotion
recognition within such environments.

通过使用交互式虚拟现实（VR）游戏和采集多模态数据（自我报告、生理和面部信号），我们使用机器学习方法确定了每个组成部分对情绪区分的独特贡献，并发现至少需要五个维度来表示我们的数据集中情绪的变化。这些发现对于在情绪研究中使用 VR 环境以及情绪识别中生理信号的作用具有重要意义。

通过交互式虚拟现实游戏在复合空间中探索情绪

Exploring Emotions in Multi-componential Space using Interactive VR  Games

Emotion regulation is a crucial element in dealing with emotional events and
has positive effects on mental health. This paper aims to provide a more
comprehensive understanding of emotional events by introducing a new French
corpus of emotional narratives collected using a questionnaire for emotion
regulation. We follow the theoretical framework of the Component Process Model
which considers emotions as dynamic processes composed of four interrelated
components (behavior, feeling, thinking and territory). Each narrative is
related to a discrete emotion and is structured based on all emotion components
by the writers. We study the interaction of components and their impact on
emotion classification with machine learning methods and pre-trained language
models. Our results show that each component improves prediction performance,
and that the best results are achieved by jointly considering all components.
Our results also show the effectiveness of pre-trained language models in
predicting discrete emotion from certain components, which reveal differences
in how emotion components are expressed.

本研究使用新的情感叙事法和机器学习方法，基于 Component Process Model 的理论框架，研究了由行为、感觉、思考和场域组成的情感构成，证明了每个情感组成都对情感分类有积极影响，同时考虑所有情感组成的综合表现是最佳的，利用预训练语言模型来预测离散情感是有效的，揭示了情感组成表达方式的不同之处。