Date of Award
Although people are good at classifying emotions, they also make mistakes. These errors tend to be negatively biased and potentially serve a protective function. Research on biases in emotion perception has largely focused on perception of individual faces and little is known about biases in evaluations of crowds. In the first investigation, I conducted six experiments, evaluating anger bias—a tendency to judge facial expressions as angry—in the context of single faces and emotionally homogenous crowds. I found that observers were biased to classify faces as angry, especially when evaluating crowds. This amplified bias emerged in the context of perceptual uncertainty and reached peak intensity for crowds with four members. Observers endorsed anger bias regardless of whether angry faces were discriminated against positive (happy) or negative (fearful) expressions. Anger bias persisted despite variability in identity and gender but was strongest for evaluations of male faces. In the second investigation, I conducted two experiments evaluating anger bias in the context of emotionally heterogenous crowds. Observers endorsed anger bias in the context of lower intensities of expression. Although, observers showed difficulty in accurately classifying crowds consisting of relatively balanced number of angry and happy faces displaying higher intensities of expression, these errors were not biased. In other words, anger bias emerged when judgments were difficult in the context of low perceptual information, but not in the context of clear and yet contradictory information. This series of studies provide insight into sensitivity and bias in crowd perception and suggest that bias is amplified in crowds in the context of scarce diagnostic information.
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Mihalache, Diana, "Anger Bias in the Evaluation of Crowds" (2021). Electronic Theses and Dissertations. 1961.
Received from ProQuest