Collaborative Research: The Angry Crowd Bias: Social, Cognitive, and Perceptual Mechanisms

University Of Denver, Denver CO

Investigators

Abstract

Most people believe they see the world and those around them accurately. However, the way people initially perceive others is often inaccurate and systematically biased towards negative perceptions. For example, when first exposed to faces that are hard to see or that have subtle expressions, people report that these faces look threatening (even when they are not). This project examines a bias to judge unfamiliar others in crowds as being angry. This research tests whether a perceiver's bias to judge others as angry depends on the others' race and gender, whether the others are alone or in a crowd, and the perceiver's own beliefs about race and gender. Racial and gender bias in crowd perception is not simply an academic issue. Crowds have been at the center stage of protest and social unrest moments that are causing vastly divergent interpretations of current events. This project reveals who may be most susceptible to negative crowd biases, the underlying visual and cognitive process that cause biased judgments, and the malleability of these biases. This research utilizes state-of-the-art methods and statistical tools to examine visual attention to faces, and bias and accuracy in emotion judgments (specifically, eye-tracking data, signal detection methods, and drift-diffusion modeling). The approach makes it possible to track visual patterns – for example, which faces people look at first in a crowd, how long they look at each face, whether they ignore anyone, whether faces appear alone or in a crowd – all of which are likely to be affected by the racial and gender features of the faces. Newly-developed materials include an extensive set of computer-generated faces that have been designed with precise variations in gender and racial features. Tracking visual patterns and judgements of these computer-generated faces can establish at what point, for whom, and why bias occurs for crowd perception. Additional materials include a representative set of crowd images from real-life settings (i.e., published in popular news sources), which help to advance an understanding of how people perceive and react to crowds they typically encounter as part of their daily lives. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.