Understanding the conditions and content of face learning underlying race perception biases

人们不善于识别来自其他种族的人的面孔，而是倾向于将他们归为一类（“他们在我看来都很像”），这就是所谓的“其他种族效应”。这种面孔认知上的种族偏见在许多社会问题中起着重要作用。例如，超过三分之一的错误定罪涉及将嫌疑人错误地识别为另一个种族，而与其他种族效应相关的隐性种族偏见导致了法律、医疗和教育系统中的歧视。证据清楚地表明，我们从某些面孔的经验中学习到其他种族效应，而不是其他面孔，但科学家们不知道这种学习是如何发生的，以及它对感知的影响。这个项目的目标是了解哪些类型的经历会导致其他种族效应的发展，以及这些经历如何改变我们对面孔的感知。我们通过使用计算机生成的面孔和种族（包括新的人工种族）测试人们来实现这一目标。通过这种方式生成人脸，我们可以精确地控制人脸的所有相关特征（例如，眼睛和鼻子的形状等）以及人们对人脸的体验。我们还以高精度测量人们的面部感知，并使用数学建模来形式化我们的假设。从这个项目中获得的知识可以更好地理解和衡量其他种族效应，为制定消除这种效应的训练程序奠定基础。该奖项反映了美国国家科学基金会的法定使命，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。

People are bad at recognizing faces when they belong to people from another race, tending instead to group them together (“they all look alike to me”), in what is known as the other-race effect. Such racial biases in face perception play a role in a number of problems in society. For example, more than a third of wrongful convictions involve misidentification of suspects for another race, and implicit racial biases that have been linked to the other-race effect lead to discrimination in the legal, medical, and educational systems. The evidence clearly shows that we learn the other-race effect from experience with some faces but not others, but scientists don’t know how that learning happens and its effects in perception. This project’s goal is to understand what types of experiences lead to development of the other-race effect, and how those experiences modify how we perceive faces. We approach this goal by testing people using computer-generated faces and races, including new artificial races. Generating faces this way allows us to precisely control all their relevant features (for example, shape of the eyes, nose, etc.) and the experience people have with them. We also measure people’s face perception with high precision and use mathematical modeling to formalize our hypotheses. Knowledge gained from this project leads to a better understanding and measurement of the other-race effect, setting the stage for the development of training procedures to eliminate it.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.