Anger bias in the evaluation of crowds.

People are good at categorizing the emotions of individuals and crowds of faces. People also make mistakes when classifying emotion. When they do so with judgments of individuals, these errors tend to be negatively biased, potentially serving a protective function. For example, a face with a subtle expression is more likely to be categorized as angry than happy. Yet surprisingly little is known about the errors people make when evaluating multiple faces. We found that perceivers were biased to classify faces as angry, especially when evaluating crowds. This amplified bias depended on uncertainty, occurring when categorization was difficult, and it reached peak intensity for crowds with four members. Drift diffusion modeling revealed the mechanisms behind this bias, including an early response component and more efficient processing of anger from crowds with subtle expressions. Our findings introduce bias as an important new dimension for understanding how perceivers make judgments about crowds. (PsycInfo Database Record (c) 2021 APA, all rights reserved).