Exploring the Component Process Model of Emotions Using Interactive Virtual Reality Games

Emotions are complex phenomena that shape our perception, behavior, and interactions. While discrete and dimensional models have been extensively studied, neuroscientific evidence better aligns with the component process model (CPM), which comprises 5 interconnected components. Despite its relevance, the CPM has received limited attention. To explore emotion processes more thoroughly, we adopted a data-driven approach using virtual reality (VR) games and collected multimodal data from 39 participants. Machine learning was employed to examine the unique contributions of each component to emotion differentiation. Our results highlight the important role of these components, with models incorporating all of them showing the strongest effects. We conclude that at least 5 dimensions are essential to represent emotions and underscore VR’s potential for emotion research.