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

Research output: Contribution to journalArticlepeer-review

1 Citation (SciVal)

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 languageEnglish
Article number202251
JournalRoyal Society Open Science
Volume8
Issue number10
Early online date13 Oct 2021
DOIs
Publication statusPublished - 31 Oct 2021

Keywords

  • affective processing
  • facial emotion
  • genetic algorithm
  • individual differences

ASJC Scopus subject areas

  • General

Fingerprint

Dive into the research topics of 'Using genetic algorithms to uncover individual differences in how humans represent facial emotion'. Together they form a unique fingerprint.

Cite this