Genetic algorithms reveal profound individual differences in emotion recognition.

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

SUBMITTER: Binetti N 

PROVIDER: S-EPMC9659399 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature