Mapping the Passions: Toward a High-Dimensional Taxonomy of Emotional Experience and Expression.


Journal

Psychological science in the public interest : a journal of the American Psychological Society
ISSN: 2160-0031
Titre abrégé: Psychol Sci Public Interest
Pays: United States
ID NLM: 100940059

Informations de publication

Date de publication:
Jul 2019
Historique:
entrez: 18 7 2019
pubmed: 18 7 2019
medline: 7 1 2020
Statut: ppublish

Résumé

What would a comprehensive atlas of human emotions include? For 50 years, scientists have sought to map emotion-related experience, expression, physiology, and recognition in terms of the "basic six"-anger, disgust, fear, happiness, sadness, and surprise. Claims about the relationships between these six emotions and prototypical facial configurations have provided the basis for a long-standing debate over the diagnostic value of expression (for review and latest installment in this debate, see Barrett et al., p. 1). Building on recent empirical findings and methodologies, we offer an alternative conceptual and methodological approach that reveals a richer taxonomy of emotion. Dozens of distinct varieties of emotion are reliably distinguished by language, evoked in distinct circumstances, and perceived in distinct expressions of the face, body, and voice. Traditional models-both the basic six and affective-circumplex model (valence and arousal)-capture a fraction of the systematic variability in emotional response. In contrast, emotion-related responses (e.g., the smile of embarrassment, triumphant postures, sympathetic vocalizations, blends of distinct expressions) can be explained by richer models of emotion. Given these developments, we discuss why tests of a basic-six model of emotion are not tests of the diagnostic value of facial expression more generally. Determining the full extent of what facial expressions can tell us, marginally and in conjunction with other behavioral and contextual cues, will require mapping the high-dimensional, continuous space of facial, bodily, and vocal signals onto richly multifaceted experiences using large-scale statistical modeling and machine-learning methods.

Identifiants

pubmed: 31313637
doi: 10.1177/1529100619850176
pmc: PMC6675572
mid: NIHMS1042548
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

69-90

Subventions

Organisme : NIMH NIH HHS
ID : T32 MH020006
Pays : United States

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Auteurs

Alan Cowen (A)

1 Department of Psychology, University of California, Berkeley.

Disa Sauter (D)

2 Faculty of Social and Behavioural Sciences, University of Amsterdam.

Jessica L Tracy (JL)

3 Department of Psychology, University of British Columbia.

Dacher Keltner (D)

1 Department of Psychology, University of California, Berkeley.

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