Using evolutionary algorithms to uncover individual differences in how humans represent facial emotion
Year: 2020
Type: preprint
Abstract: How humans perceive and represent emotional facial expressions critically guides social interactions and has a profound impact on social cognition. However, much emotion research to date is potentially flawed by relying on the assumption that people represent categorical emotions in the same way, using standardised stimulus sets and therefore overlooking important individual differences in emotion processing. To resolve this problem, we developed a task using Genetic Algorithms and derived participant-generated emotional expressions from 105 individuals. A separate group (N=108) was then asked to identify the expression on these faces. Taken together, results showed relative population-level consistency in the representation of happy faces alongside a high degree of variability in the representation of fear and sadness. High test-retest reliability was observed. This novel method enables the efficient quantification of variation in how people represent emotional expressions, presenting promising advances for the study of individual differences in social cognition and emotion processing.
Authors: Christina Carlisi, Kyle Reed, Fleur G L Helmink, Robert Lachlan, Darren P Cosker, Essi Viding, Isabelle Mareschal
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
Awards: Towards an individualised, mechanistic understanding of resilience to mental illness during development: the role of affective processing in adolescence, G339059223, Understanding individual differences in facial emotion perception and their association with psychiatric risk indicators, EPSRC Centre for Doctoral Training in Digital Entertainment, Centre for the Analysis of Motion, Entertainment Research and Applications (CAMERA)
OpenAlex ID: W4239376166