Computational approaches to the neuroscience of social perception

Jeffrey A. Brooks, Ryan M. Stolier, Jonathan B. Freeman

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)827-837
Number of pages11
JournalSocial cognitive and affective neuroscience
Issue number8
StatePublished - Aug 1 2021


  • computational neuroscience
  • emotion
  • multivariate pattern analysis
  • social cognition
  • social perception

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • Cognitive Neuroscience


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