Spontaneous Neural Encoding of Social Network Position
Nature Human Behavior, Forthcoming
41 Pages Posted: 9 Jan 2017 Last revised: 8 Feb 2017
Date Written: February 6, 2017
Unlike many species that enact social behavior in loose aggregations (e.g., swarms, herds), humans form groups comprised of many long-term, intense, non-reproductive bonds with non-kin. The cognitive demands of navigating such groups are thought to have significantly influenced human brain evolution. Yet, little is known about how and to what extent the human brain encodes the structure of the social networks in which it is embedded. We characterized the social network of an academic cohort (N=277); a subset (N=21) completed a functional magnetic resonance imaging (fMRI) study involving viewing individuals who varied in terms of “degrees of separation” from themselves (social distance), the extent to which they are well-connected to well-connected others (eigenvector centrality - EC), and the extent to which they connect otherwise unconnected individuals (brokerage). Understanding these social network position characteristics requires tracking direct relationships, bonds between third parties, and the broader network topology. Pairing network data with multi-voxel pattern analysis, we show that social network position information is accurately perceived and spontaneously activated upon encountering familiar individuals. These findings elucidate how the human brain encodes the structure of its social world, and underscore the importance of integrating an understanding of social networks into the study of social perception.
Keywords: Social Network Analysis, Social Perception, Functional MRI, Multi-Voxel Pattern Analysis
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