How do people learn the large and complex network of social relationships around them? We test how people use information about social traits (such as being part of the same club or sharing hobbies) to fill in gaps in their knowledge of friendships and to make inferences about unobserved friendships in the social network. We find that the ability to infer friendships depends on simple but inflexible processes that infer friendship when two people share the same features, and a more complex and flexible cognitive map that encodes interpersonal rather than interpersonal relationships. Our results reveal that cognitive maps play a powerful role in shaping the way people represent and interpret relationships in the social network.
In order to navigate a complex web of relationships, an individual must learn and represent the connections between people in a social network. However, the sheer size and complexity of the social world makes it impossible to gain direct knowledge of all the relationships within the network, suggesting that people must make inferences about unobserved relationships to fill in the gaps. across three studies (n = 328), we show that people can encode information about social traits (eg, hobbies, clubs) and then disseminate this knowledge to infer the existence of unobserved friendships in the network. Using computational models, we test various feature-based mechanisms that can support such inferences. We found that people’s ability to generalize successfully depends on two representative strategies: a simple but inelastic similarity inference that takes advantage of same-sex relationships, and a complex but flexible cognitive map that encodes the statistical relationships between social traits and friendships. Together, our studies revealed that people can generate cognitive maps that encode random patterns of latent relationships in many abstract feature spaces, allowing social networks to be represented in a flexible format. Moreover, these findings shed light on open questions across disciplines about how people learn and represent social networks and may have implications for generating more human-like association predictions in machine learning algorithms.
Author contributions: Research designed by J.-YS, AB, and OFH; J-YS conducted research; Data analyzed by J.-YS, AB and OFH; J.-YS, AB, and OFH wrote the paper.
The book declares no competing interest.
This article is a direct PNAS submission.
This article contains supporting information online at https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.2021699118/-/DCSupplemental.
The data and code supporting the results of this manuscript are available online in the Open Science Framework at the following URL: https://osf.io/v8ucz/.