Name
Abstract | ||
|---|---|---|
Emotions are 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 emphasise 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 characterise emotional experiences evoked during movie watching. 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 limited number of descriptors is still able to predict the level of experienced discrete emotion(s) to a satisfactory level. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACII.2019.8925491 | 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII) |
Keywords | Field | DocType |
emotion,component model,emotion mechanism,emotion dimensions,data-driven approach,computational modelling,emotional experience,emotion recognition | Social psychology,Discrete emotions,Discrete category,Computer science,Cognitive psychology,Multiple component,Theoretical models,Feeling,Empirical research | Conference |
ISSN | ISBN | Citations |
2156-8103 | 978-1-7281-3889-3 | 0 |
PageRank | References | Authors |
0.34 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gelareh Mohammadi | 1 | 257 | 13.37 |
| Kangying Lin | 2 | 0 | 0.34 |
| P Vuilleumier | 3 | 435 | 40.82 |