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

Christina O. Carlisi; Kyle Reed; Fleur G.L. Helmink; Robert Lachlan; Darren P. Cosker; Essi Viding; Isabelle Mareschal

doi:10.1098/rsos.202251

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

Christina O. Carlisi^*, Kyle Reed, Fleur G.L. Helmink, Robert Lachlan, Darren P. Cosker, Essi Viding, Isabelle Mareschal

^*Corresponding author for this work

Child and Adolescent Psychiatry / Psychology

Research output: Contribution to journal › Article › Academic › peer-review

6 Citations (Scopus)

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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.

Original language	English
Article number	202251
Journal	Royal Society Open Science
Volume	8
Issue number	10
DOIs	https://doi.org/10.1098/rsos.202251
Publication status	Published - 1 Oct 2021

Bibliographical note

Funding Information:
C.O.C. received support as a Wellcome Trust Postdoctoral Fellow (grant no. 206459/Z/17/Z). K.R.'s doctoral studentship was funded by the EPSRC (grant no. EP/L016540/1). D.P.C., E.V. and I.M. received support from the MRC (grant no. MR/S011307/1). D.P.C. directs the Centre for the Analysis of Motion, Entertainment Research and Applications (EPSRC EP/M023281/1). E.V. was supported by a British Academy Mid-Career Fellowship.

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Using genetic algorithms to uncover individual differences in how humans represent facial emotionFinal published version, 1.15 MBLicence: CC BY

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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. ",

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

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