Knowledge of someone's friendships can powerfully impact how one interacts with them. Previous research suggests that information about others' real-world social network positions-e.g. how well-connected they are (centrality), 'degrees of separation' (relative social distance)-is spontaneously encoded when encountering familiar individuals. However, many types of information covary with where someone sits in a social network. For instance, strangers' face-based trait impressions are associated with their social network centrality, and social distance and centrality are inherently intertwined with familiarity, interpersonal similarity and memories. To disentangle the encoding of the social network position from other social information, participants learned a novel social network in which the social network position was decoupled from other factors and then saw each person's image during functional magnetic resonance imaging scanning. Using representational similarity analysis, we found that social network centrality was robustly encoded in regions associated with visual attention and mentalizing. Thus, even when considering a social network in which one is not included and where centrality is unlinked from perceptual and experience-based features to which it is inextricably tied in naturalistic contexts, the brain encodes information about others' importance in that network, likely shaping future perceptions of and interactions with those individuals.
Keywords: fMRI; representational similarity analysis; social networks.
© The Author(s) 2023. Published by Oxford University Press.