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

该项目扩展和多样化了芝加哥脸部数据库--一个免费提供高质量面部刺激的来源，可供科学研究使用。该数据库被研究界广泛用于研究各种领域感兴趣的现象，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，在人们在社交世界中导航时至关重要。婴儿来到这个世界，准备好适应和照顾人类的面孔。成年后，人类学会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他人的大量有心理意义的特征。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、增强并向研究人员提供了这样的刺激。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，并在科学方面培训历史上代表性不足的学生。芝加哥人脸数据库目前包括800多名亚洲人、黑人、拉丁裔、多种族、白人美国人和南亚印第安人的数字图像。它还包括脸部的重要评级和体格测量。芝加哥人脸数据库旨在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人脸刺激。自2015年发布以来，芝加哥人脸数据库已经被下载超过8000次，被引用超过1400次，肯定了它对科学界的价值。目前的项目对芝加哥人脸数据库进行了扩展和多样化，纳入了另外200张在中国和日本拍摄的人的图像，更新了人脸的主观评级，提供了更全面的人脸测量，并设计了新的基于网络的工具，用于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认知缺陷相关的基础科学问题，这是一种心理现象，即人们对属于自己种族的面孔的认知度高于对其他种族的认知度。此外，还为攻读计算机科学的历史上代表性不足的学生提供了一个高影响力的亲身实践指导计划。学生在与计算机科学家密切合作开发基于网络的工具时，将接受专业发展指导和个性化指导。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.