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Using genetic algorithms to uncover individual differences in how humans represent facial emotion.

Authors
  • Carlisi, Christina O1
  • Reed, Kyle2
  • Helmink, Fleur G L3
  • Lachlan, Robert4
  • Cosker, Darren P2
  • Viding, Essi1
  • Mareschal, Isabelle5
  • 1 Division of Psychology and Language Sciences, Developmental Risk and Resilience Unit, University College London, 26 Bedford Way, London WC1H 0AP, UK.
  • 2 Department of Computer Science, University of Bath, 1 West, Claverton Down, Bath BA2 7AY, UK.
  • 3 Erasmus University Medical Center, s-Gravendijkwal 230, Rotterdam 3015 CE, The Netherlands. , (Netherlands)
  • 4 Department of Psychology, Royal Holloway University of London, Wolfson Building, Egham TW20 0EX, UK.
  • 5 School of Biological and Chemical Sciences, Department of Psychology, Queen Mary University of London, G. E. Fogg Building, Mile End Road, London E1 4DQ, UK.
Type
Published Article
Journal
Royal Society Open Science
Publisher
The Royal Society
Publication Date
Oct 01, 2021
Volume
8
Issue
10
Pages
202251–202251
Identifiers
DOI: 10.1098/rsos.202251
PMID: 34659775
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Emotional facial expressions critically impact social interactions and cognition. However, emotion research to date has generally relied on the assumption that people represent categorical emotions in the same way, using standardized stimulus sets and overlooking important individual differences. To resolve this problem, we developed and tested a task using genetic algorithms to derive assumption-free, participant-generated emotional expressions. One hundred and five participants generated a subjective representation of happy, angry, fearful and sad faces. Population-level consistency was observed for happy faces, but fearful and sad faces showed a high degree of variability. High test-retest reliability was observed across all emotions. A separate group of 108 individuals accurately identified happy and angry faces from the first study, while fearful and sad faces were commonly misidentified. These findings are an important first step towards understanding individual differences in emotion representation, with the potential to reconceptualize the way we study atypical emotion processing in future research. © 2021 The Authors.

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