The accurate judgment of social network characteristics in the lab and field using thin slices of the behavioral stream

Abstract

When deciding whom to ally with or avoid, people benefit from assessing the quantity and quality of strangers’ relationships with others. How accurately do people make such social network assessments? Across three lab studies and one preregistered field study, we tested whether people (total N = 1545) could make accurate judgments about a stranger’s (total N = 709) social network characteristics after watching brief thin slice videos of the stranger or negotiating with them. The findings consistently demonstrated that perceivers accurately detected the size of a stranger’s social networks and their gender and family composition, based on theoretically relevant social-behavioral tendencies and traits (e.g., extraversion, gender), but not how interconnected these social networks were. Perceivers also missed cues that could have facilitated greater accuracy. These data advance theory about adaptive social decision making in psychology, network science, sociology, and organizational behavior. We also provide the freely available Social Network Accuracy Test (SNAT) for future research: (https://osf.io/zgbse).