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

合作研究：愤怒的人群偏见：社会、认知和感知机制

• 批准号: 2141313
• 负责人: Timothy Sweeny
• 金额: $ 30.27万
• 依托单位: University of Denver
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2141313&HistoricalAwards=false
• 关键词: Collaborative; Research; Angry; Crowd; Bias

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.

大多数人相信他们能准确地看到世界和周围的人。然而，人们最初看待他人的方式往往是不准确的，并系统地偏向于负面看法。例如，当第一次接触到难以看到或有微妙表情的面孔时，人们报告说这些面孔看起来具有威胁性（即使它们不是）。这个项目研究了一种偏见，即把人群中不熟悉的人判断为愤怒。这项研究测试了感知者判断他人愤怒的偏见是否取决于他人的种族和性别，其他人是独自一人还是在人群中，以及感知者自己对种族和性别的信念。人群感知中的种族和性别偏见不仅仅是一个学术问题。人群一直处于抗议和社会动荡时刻的中心舞台，这导致了对当前事件的截然不同的解释。这个项目揭示了谁可能最容易受到负面人群偏见的影响，导致偏见判断的潜在视觉和认知过程，以及这些偏见的可塑性。这项研究利用最先进的方法和统计工具来检查对面孔的视觉注意，以及情绪判断的偏差和准确性（具体来说，眼动跟踪数据，信号检测方法和漂移扩散模型）。这种方法可以跟踪视觉模式-例如，人们在人群中首先看到的面孔，他们看每张面孔的时间，他们是否忽略任何人，面孔是否单独出现或出现在人群中-所有这些都可能受到种族和性别特征的影响。新开发的材料包括一套广泛的计算机生成的面孔，这些面孔在性别和种族特征上有着精确的变化。跟踪这些计算机生成的面孔的视觉模式和判断可以确定在什么时候，为谁，以及为什么偏见发生在人群感知中。额外的材料包括来自现实生活环境的一组代表性的人群图像（即，这一奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。