  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.
