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

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.

本研究利用大型语言模型（LLMs）的人工神经元研究人类情感推断机制，发现 LLMs 基于情感知识的概念结构与人类行为中的离散情感的结构相似，且它基于 14 个概念属性的情感推断依赖程度与人类依赖程度相近，这为情感知识在大型语言模型中的出现提供了直接证据，并建议其对离散情感推断至关重要。