  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.

Neurons and Cognition;Artificial Intelligence;Machine Learning;Robotics