A Multi-Componential Approach to Emotion Recognition and the Effect of Personality

TL;DR

This study introduces a multi-componential framework for emotion recognition, demonstrating that emotions can be characterized by a few latent dimensions linked to component processes, and explores how personality influences emotional experiences.

Contribution

The paper applies a data-driven, componential approach to model emotions and investigates the relationship between discrete emotions, component features, and personality traits.

Findings

Six latent dimensions capture emotion differences.

Component features predict discrete emotions effectively.

Personality traits influence emotion components and experiences.

Abstract

Emotions are an inseparable part of human nature affecting our behavior in response to the outside world. Although most empirical studies have been dominated by two theoretical models including discrete categories of emotion and dichotomous dimensions, results from neuroscience approaches suggest a multi-processes mechanism underpinning emotional experience with a large overlap across different emotions. While these findings are consistent with the influential theories of emotion in psychology that emphasize a role for multiple component processes to generate emotion episodes, few studies have systematically investigated the relationship between discrete emotions and a full componential view. This paper applies a componential framework with a data-driven approach to characterize emotional experiences evoked during movie watching. The results suggest that differences between various emotions can be captured by a few (at least 6) latent dimensions, each defined by features associated with component processes, including appraisal, expression, physiology, motivation, and feeling. In addition, the link between discrete emotions and component model is explored and results show that a componential model with a limited number of descriptors is still able to predict the level of experienced discrete emotion(s) to a satisfactory level. Finally, as appraisals may vary according to individual dispositions and biases, we also study the relationship between personality traits and emotions in our computational framework and show that the role of personality on discrete emotion differences can be better justified using the component model.