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

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.

SUBMITTER: Carlisi CO

PROVIDER: S-EPMC8511778 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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

Carlisi Christina O CO Reed Kyle K Helmink Fleur G L FGL Lachlan Robert R Cosker Darren P DP Viding Essi E Mareschal Isabelle I

Royal Society open science 20211013 10

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 <i>genetic algorithms</i> to derive assumption-free, participant-generated emotional expressions. One hundred and five participants generate ...[more]

PMID: 34659775

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Project description:Facial identity and facial expression processing are crucial socio-emotional abilities but seem to show only limited psychometric uniqueness when the processing speed is considered in easy tasks. We applied a comprehensive measurement of processing speed and contrasted performance specificity in socio-emotional, social and non-social stimuli from an individual differences perspective. Performance in a multivariate task battery could be best modeled by a general speed factor and a first-order factor capturing some specific variance due to processing emotional facial expressions. We further tested equivalence of the relationships between speed factors and polymorphisms of dopamine and serotonin transporter genes. Results show that the speed factors are not only psychometrically equivalent but invariant in their relation with the Catechol-O-Methyl-Transferase (COMT) Val158Met polymorphism. However, the 5-HTTLPR/rs25531 serotonin polymorphism was related with the first-order factor of emotion perception speed, suggesting a specific genetic correlate of processing emotions. We further investigated the relationship between several components of event-related brain potentials with psychometric abilities, and tested emotion specific individual differences at the neurophysiological level. Results revealed swifter emotion perception abilities to go along with larger amplitudes of the P100 and the Early Posterior Negativity (EPN), when emotion processing was modeled on its own. However, after partialling out the shared variance of emotion perception speed with general processing speed-related abilities, brain-behavior relationships did not remain specific for emotion. Together, the present results suggest that speed abilities are strongly interrelated but show some specificity for emotion processing speed at the psychometric level. At both genetic and neurophysiological levels, emotion specificity depended on whether general cognition is taken into account or not. These findings keenly suggest that general speed abilities should be taken into account when the study of emotion recognition abilities is targeted in its specificity.

Dataset Information

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

Publications

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

Similar Datasets

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