Spontaneous neural encoding of social network position

Abstract

Unlike many species that enact social behaviour in loose aggregations (such as swarms or herds), humans form groups comprising many long-term, intense, non-reproductive bonds with non-kin1. The cognitive demands of navigating such groups are thought to have significantly influenced human brain evolution2. 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 = 275); 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 were well-connected to well-connected others (eigenvector centrality) and the extent to which they connected otherwise unconnected individuals (brokerage). Understanding these characteristics of social network position requires tracking direct relationships, bonds between third parties and the broader network topology. Pairing network data with multi-voxel pattern analysis, we show that information about social network position is accurately perceived and spontaneously activated when 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. Parkinson et al. combine social network analysis and multi-voxel pattern analysis of functional magnetic resonance imaging data to show that the brain spontaneously encodes social distance, the centrality of the individuals encountered, and the extent to which they serve to broker connections between members.