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Abstract
Emotional communication relies on a mutual understanding, between expresser and viewer, of facial configurations that broadcast specific emotions. However, we do not know whether people share a common understanding of how emotional states map onto facial expressions. This is because expressions exist in a high-dimensional space too large to explore in conventional experimental paradigms. Here, we address this by adapting genetic algorithms and combining them with photorealistic three-dimensional avatars to efficiently explore the high-dimensional expression space. A total of 336 people used these tools to generate facial expressions that represent happiness, fear, sadness, and anger. We found substantial variability in the expressions generated via our procedure, suggesting that different people associate different facial expressions to the same emotional state. We then examined whether variability in the facial expressions created could account for differences in performance on standard emotion recognition tasks by asking people to categorize different test expressions. We found that emotion categorization performance was explained by the extent to which test expressions matched the expressions generated by each individual. Our findings reveal the breadth of variability in people's representations of facial emotions, even among typical adult populations. This has profound implications for the interpretation of responses to emotional stimuli, which may reflect individual differences in the emotional category people attribute to a particular facial expression, rather than differences in the brain mechanisms that produce emotional responses.
Original language | English |
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Article number | e2201380119 |
Journal | Proceedings of the National Academy of Sciences of the United States of America |
Volume | 119 |
Issue number | 45 |
Early online date | 2 Nov 2022 |
DOIs | |
Publication status | Published - 8 Nov 2022 |
Bibliographical note
This study was funded by a Medical Research Council (MRC) Grant, MR/R01177X/1, awarded to I.M., E.V., and D.C. We thank Samuel Solomon for his insightful contributions to the structuring of the manuscript and Rosie Joy Duffy, Francesca Procopio, and Tori Sasaki for assisting with data collection.Funding
ACKNOWLEDGMENTS. This study was funded by a Medical Research Council (MRC) Grant, MR/R01177X/1, awarded to I.M., E.V., and D.C. We thank Samuel Solomon for his insightful contributions to the structuring of the manuscript and Rosie Joy Duffy, Francesca Procopio, and Tori Sasaki for assisting with data collection.
Keywords
- emotion recognition
- faces
- facial expressions
- genetic algorithms
- machine learning
ASJC Scopus subject areas
- General
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Centre for the Analysis of Motion, Entertainment Research and Applications (CAMERA) - 2.0
Campbell, N. (PI), Cosker, D. (PI), Bilzon, J. (CoI), Campbell, N. (CoI), Cazzola, D. (CoI), Colyer, S. (CoI), Cosker, D. (CoI), Lutteroth, C. (CoI), McGuigan, P. (CoI), O'Neill, E. (CoI), Petrini, K. (CoI), Proulx, M. (CoI) & Yang, Y. (CoI)
Engineering and Physical Sciences Research Council
1/11/20 → 31/10/25
Project: Research council
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Centre for the Analysis of Motion, Entertainment Research and Applications (CAMERA)
Cosker, D. (PI), Bilzon, J. (CoI), Campbell, N. (CoI), Cazzola, D. (CoI), Colyer, S. (CoI), Fincham Haines, T. (CoI), Hall, P. (CoI), Kim, K. I. (CoI), Lutteroth, C. (CoI), McGuigan, P. (CoI), O'Neill, E. (CoI), Richardt, C. (CoI), Salo, A. (CoI), Seminati, E. (CoI), Tabor, A. (CoI) & Yang, Y. (CoI)
Engineering and Physical Sciences Research Council
1/09/15 → 28/02/21
Project: Research council