Assessing the Impacts of Social Categorization on Person Perception and Behavior: A Formal Modeling Approach

人们根据自己所属的社会群体对他人进行分类。这是社会认知和判断的基本原则。当这些类别与群体刻板印象和偏见联系在一起时，社会分类往往会产生负面后果。它会影响人们思考、判断和对待他人的方式。它极大地助长了歧视、排斥和群体间的敌意。社会心理学的理论和研究在理解社会刻板印象和人的知觉方面取得了许多进展。这一领域的一个挑战是很难衡量判断和行为在多大程度上受到社会类别与其他类型的个人信息的影响。重要的是要更好地区分这些影响来源，以便更充分地了解群体偏见的起源。本项目开发了一个模型来更直接地衡量社会类别和其他个人属性的独立贡献。更大的目标是更好地理解导致歧视、社会排斥、群体间敌意和健康差距的社会偏见的本质。本项目通过发展和应用多项处理树（Multinomial Processing Tree， MPT）模型来更直接地衡量社会类别和其他个人属性的独立贡献，从而推进社会分类和刻板印象的研究。该模型用于检验关于增加或减少社会分类和刻板印象程度的条件的特定假设。个人感知的基础理论认为，当感知者缺乏动机或能力来仔细关注他人时，社会类别最具影响力。该模型确定了在这些情况下，偏见的增加在多大程度上反映了刻板印象的使用增加，个人特征的使用减少，或两者兼而有之。这些数据被用来评估现有的个人感知理论，并发展新的理论。这些数据还有助于表明何时不同的减少偏倚干预措施可能最有效。由于该模型提供了一种单独衡量社会类别和个人属性影响的方法，它允许仔细评估哪种策略在什么情况下最有效地减少偏见。该项目还通过开发和共享一种用于测量和分析行为数据的新型仪器，改善了科学基础设施。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 categorize others according to the social groups to which they belong. This is a fundamental principle of social perception and judgment. Social categorization often has negative consequences when those categories are associated with group stereotypes and prejudices. It can affect the ways in which people think about, judge, and behave toward other people. It contributes significantly to discrimination, exclusion, and intergroup hostility. Theory and research in social psychology has provided many advances in understanding social stereotypes and person perception. One challenge in this area has been the difficulty in measuring the extent to which judgments and behavior are influenced by social categories versus other kinds of personal information. It is important to better distinguish among these sources of influence to understand the origins of group bias more fully. This project develops a model to measure the independent contributions of social categories and other personal attributes more directly. The larger aim is to better understand the nature of social bias that leads to discrimination, social exclusion, intergroup hostility, and health disparities.This project advances the study of social categorization and stereotyping by developing and applying a Multinomial Processing Tree (MPT) model to more directly measure the independent contributions of social categories and other personal attributes. The model is used to test specific hypotheses about the conditions that increase or decrease the extent of social categorization and stereotyping. Foundational theories of person perception propose that social categories are most impactful when perceivers lack either the motivation or ability to pay careful attention to others. The model identifies the extent to which increased bias in those situations reflects greater use of stereotypes, diminished use of personal features, or both. These data are used to evaluate existing theories of person perception and to develop novel theories. The data also help to indicate when different bias reduction interventions may be most effective. Because the model offers a way of separately measuring the impacts of social categories and personal attributes, it permits a careful assessment of which strategies are most effective for reducing bias and in what contexts. The project also improves science infrastructure by developing and sharing a novel instrument for measuring and analyzing behavioral data.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.