SBP: Collaborative Research: RUI: Expansion and Infrastructure Development of the Chicago Face Database

这个项目扩展和多样化了芝加哥面部数据库-一个免费提供高质量面部刺激的科学研究来源。该数据库被研究界广泛用于研究各个领域感兴趣的现象，包括心理学、计算机科学和经济学。人脸提供了丰富的信息来源，对人们在社交世界中导航至关重要。婴儿来到这个世界上，就已经准备好去定位和注意人类的面孔了。到了成年，人类学会了优先考虑那些可以根据性别、年龄和种族对他人进行快速分类的特征。人们根据面部特征推断出大量有心理意义的特质。有效地研究人脸加工及其对人类行为的下游影响需要高质量、有效的刺激。该项目扩展、增强并为研究人员提供这些刺激。通过改进芝加哥人脸数据库，该项目促进了更好的科学实践，帮助研究人员测试新的和不同的问题，追求基础科学问题，并培养历史上代表性不足的科学学生。芝加哥人脸数据库目前包括800多张亚裔、黑人、拉丁裔、多种族、美国白人和南亚印第安人的数字图像。它还包括重要的评分和面部的物理测量。芝加哥人脸数据库的设计是为了满足一个重要的研究需求：免费为研究人员提供高质量和多样化的面部刺激。自2015年发布以来，芝加哥人脸数据库已被下载超过8000次，被引用超过1400次，这肯定了它对科学界的价值。目前的项目扩大了芝加哥人脸数据库并使其多样化，增加了200张在中国和日本拍摄的人物图像，更新了人脸的主观评分，提供了更全面的人脸测量，并设计了新的基于网络的工具来访问资源和促进刺激选择。为了实现这些目标，该项目解决了与跨种族识别缺陷相关的基础科学问题。跨种族识别缺陷是一种心理现象，人们对自己种族的面孔比其他种族的面孔更容易识别。此外，一个高影响力，动手指导计划提供给历史上代表性不足的学生攻读计算机科学。学生在与计算机科学家密切合作开发基于网络的工具时，接受专业发展指导和个性化指导。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。

This project expands and diversifies the Chicago Face Database – a source for high-quality facial stimuli freely available for scientific research. The database is utilized extensively by the research community to study phenomena of interest to a variety of fields, including psychology, computer science, and economics. Human faces offer a rich source of information and are critically important as people navigate the social world. Babies enter the world ready to orient and attend to human faces. By adulthood, humans learn to prioritize the features that allow for the rapid categorization of others by sex, age, and race. People infer a large number of psychologically meaningful attributes about others based on facial features. Effectively studying face processing and its downstream effects on human behavior requires high-quality, valid stimuli. This project expands, enhances and makes available such stimuli to researchers. By improving the Chicago Face Database, this project promotes better scientific practices, helps researchers test new and different questions, pursues basic science questions, and trains historically underrepresented students in the sciences.The Chicago Face Database currently includes digital images of over 800 Asian, Black, Latinx, multiracial, and White Americans and South Asian Indians. It also includes important ratings and physical measures of the faces. The Chicago Face Database was designed to fill a significant research need: providing high-quality and diverse face stimuli to researchers at no cost. Since being released in 2015, the Chicago Face Database has been downloaded over 8,000 times and cited over 1,400 times, affirming its value to the scientific community. The current project expands and diversifies the Chicago Face Database to include 200 additional images of people photographed in China and Japan, update the subjective ratings of the faces, provide more comprehensive measurements of the faces, and design new web-based tools for accessing the resource and facilitating stimulus selection. In accomplishing these goals, the project addresses basic science questions related to the cross-race recognition deficit, a psychological phenomenon whereby people show better recognition for faces belonging to their own race than to other races. In addition, a high-impact, hands-on mentoring program is offered to historically underrepresented students pursuing computer science. The students receive professional development coaching and individualized mentoring as they work closely with a computer scientist in the development of the web-based tools.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.