Delaney McDonagh¹, Darla Bonagura², Timothy Sweeny¹, Gorkem Er¹, Sarah Lamer²; ¹University of Denver, ²University of Tennessee Knoxville

Crowds hold a unique place in perception; people are adept at making rapid and precise judgments about clear, prototypical emotions. Facial expressions, however, are often nuanced, and the judgements people make about these expressions are thus made with an unavoidable degree of uncertainty. When making low-confidence evaluations, perceivers are typically biased to interpret facial expressions as negative, which potentially serves a protective function. When people make judgments about crowds—which are associated with increased potential for threat—negativity bias is even further amplified. While this bias has been observed with tightly controlled stimuli (e.g., faces portraying posed expressions with no visual context), we hypothesized that this bias would extend to naturally occurring scenes where visual complexity increases ambiguity (e.g., context, demographic composition). To test this, we selected images (N=1972) featuring crowds and individuals from 7 news sources (e.g., NPR, National Geographic). All images were normed by coders (N=248) who rated the anger or happiness of people visible in vertical slices of each crowd or individual image. In a within-subjects design, participants (N=265) viewed 250 angry and happy crowds and individuals for 1s and rated the emotional intensity of the image from 0-9. Participants were randomly assigned to rate the images on anger or happiness. Relative to the norm ratings, participants overestimated the emotional intensity of angry crowds more than angry individuals, but there was no amplification effect for happy crowds. Thus, crowds – even ones containing visual complexity – potentiate perceivers’ tendency to overestimate the anger of others.