Department of Psychology, University of Denver.
Department of Psychology, University of Tennessee.
J Exp Psychol Gen. 2021 Sep;150(9):1870-1889. doi: 10.1037/xge0001025. Epub 2021 Feb 4.
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).
人们擅长对个体和群体的面部表情进行分类。在进行情绪分类时，人们也会犯错。当他们对个体进行判断时，这些错误往往带有负面偏见，可能具有保护作用。例如，一个表情微妙的脸更有可能被归类为生气而不是高兴。然而，人们在评估多张面孔时所犯的错误却鲜为人知。我们发现，观察者往往会将面孔归类为愤怒，尤其是在评估群体时。这种放大的偏见取决于不确定性，即分类困难时出现，并且对于有四个成员的群体达到峰值强度。漂移扩散模型揭示了这种偏见背后的机制，包括早期反应成分和对表情微妙的群体中愤怒的更有效处理。我们的研究结果将偏见作为理解观察者如何对群体做出判断的一个重要新维度。
