Computational approaches to the neuroscience of social perception.
computational neuroscience
emotion
multivariate pattern analysis
social cognition
social perception
Journal
Social cognitive and affective neuroscience
ISSN: 1749-5024
Titre abrégé: Soc Cogn Affect Neurosci
Pays: England
ID NLM: 101288795
Informations de publication
Date de publication:
05 08 2021
05 08 2021
Historique:
received:
13
11
2019
revised:
23
07
2020
accepted:
09
09
2020
pubmed:
29
9
2020
medline:
21
10
2021
entrez:
28
9
2020
Statut:
ppublish
Résumé
Across multiple domains of social perception-including social categorization, emotion perception, impression formation and mentalizing-multivariate pattern analysis (MVPA) of functional magnetic resonance imaging (fMRI) data has permitted a more detailed understanding of how social information is processed and represented in the brain. As in other neuroimaging fields, the neuroscientific study of social perception initially relied on broad structure-function associations derived from univariate fMRI analysis to map neural regions involved in these processes. In this review, we trace the ways that social neuroscience studies using MVPA have built on these neuroanatomical associations to better characterize the computational relevance of different brain regions, and discuss how MVPA allows explicit tests of the correspondence between psychological models and the neural representation of social information. We also describe current and future advances in methodological approaches to multivariate fMRI data and their theoretical value for the neuroscience of social perception.
Identifiants
pubmed: 32986115
pii: 5912490
doi: 10.1093/scan/nsaa127
pmc: PMC8343569
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
827-837Subventions
Organisme : NIMH NIH HHS
ID : R01 MH112640
Pays : United States
Informations de copyright
© The Author(s) 2020. Published by Oxford University Press.
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