UCLA

Prior research has shown human observers extract perceptual summaries for sets of items after brief visual exposure, accurately judging the average size of geometric shapes (Ariely, 2001) or the walking direction of a crowd (Sweeny, Haroz, & Whitney, 2013). Beyond actuarial summaries, I hypothesized that observers extract social information about groups that may influence downstream judgments and behavior. In Study Set One, I showed that humans accurately perceive the sex ratio of a group after only 500 millisecond visual exposure. I then tested whether these percepts bias judgments about the group’s social attitudes and affect the perceiver’s sense of belonging. As the ratio of men to women increased, perceivers judged the group to harbor more sexist norms, and judgments of belonging changed concomitantly, albeit in opposite directions for men and women. Using reverse correlation, I also demonstrated that majority-male groups elicit a mental representation perceived as angrier and less approachable than the average majority-female group member.

In Study Set Two, I examined whether the ensemble coding process replicates with real-world groups. Using stimuli derived from panels presenting at the 2015 Society for Personality and Social Psychology Annual Meeting, I showed that perceivers rapidly extract actuarial and social summaries from 4-person groups. I next probed whether ensemble coding judgments extend to attributions about the group’s broader impacts, finding that male-dominated panels were rated as less likely to mentor underrepresented minorities and disseminate their work publicly. Thus, observers judge a group’s sex ratio from a mere glimpse, inferring from it social attitudes and interpersonal affordances.

In the final chapter, I described a novel open-source Python framework I created to test my hypotheses. Platform-independent, the Social Vision Toolkit facilitates experimental stimuli presentation and exists free of charge. Although researchers must write their own scripts, the Social Vision Toolkit includes several demo experiments and code annotation to aid new programmers. It can be downloaded from an open-access GitHub repository, allowing users to adapt the code and customize features to suit their needs. The Social Vision Toolkit enables greater methodological transparency and collaboration, as well as potentially broadens the scope of what is experimentally possible.