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Genetic Algorithms reveal profound individual differences in emotion recognition

Binetti, Nicola; Roubtsova, Nadejda; Carlisi, Christina; Cosker, Darren; Viding, Essi; Mareschal, Isabelle; (2022) Genetic Algorithms reveal profound individual differences in emotion recognition. Proceedings of the National Academy of Sciences (PNAS) - Psychological and Cognitive Sciences , 119 (45) , Article e2201380119. 10.1073/pnas.220138011. Green open access

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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.

Type: Article
Title: Genetic Algorithms reveal profound individual differences in emotion recognition
Open access status: An open access version is available from UCL Discovery
DOI: 10.1073/pnas.220138011
Publisher version: https://doi.org/10.1073/pnas.2201380119
Language: English
Additional information: Copyright © 2022 the Author(s). Published by PNAS. This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences > Clinical, Edu and Hlth Psychology
URI: https://discovery.ucl.ac.uk/id/eprint/10159166
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