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

评估社会分类对人的感知和行为的影响：正式的建模方法

• 批准号: 2215236
• 负责人: Jeffrey Sherman
• 金额: $ 50万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2215236&HistoricalAwards=false
• 关键词: Assessing; Impacts; Social; Categorization; Person

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.

人们根据自己所属的社会群体对他人进行分类。这是社会感知和判断的基本原则。当这些类别与群体刻板印象和偏见相关联时，社会分类通常会产生负面影响。它可以影响人们对他人的思考，判断和行为的方式。它有助于歧视，排斥和群体间的敌对行为。社会心理学中的理论和研究在理解社会刻板印象和人的看法方面为许多进步提供了许多进步。在这一领域的一个挑战是难以衡量判断和行为受社会类别与其他类型的个人信息影响的程度。更好地区分这些影响力很重要，以便更充分地了解群体偏见的起源。该项目开发了一个模型，以更直接地衡量社会类别和其他个人属性的独立贡献。更大的目的是更好地了解社会偏见的本质，从而导致歧视，社会排斥，群体间敌对和健康差异。该项目通过开发和应用多项式加工树（MPT）模型来促进社会分类和刻板印象的研究，以更直接地衡量社会类别和其他个人属性的独立贡献和其他个人属性。该模型用于测试有关增加或减少社会分类和刻板印象程度的条件的特定假设。人观念的基本理论提出，当感知者缺乏动力或能够仔细关注他人的能力时，社会类别最具影响力。该模型确定在这些情况下增加偏见的程度反映了对刻板印象的更大使用，个人特征的使用减少或两者兼而有之。这些数据用于评估人类感知的现有理论并发展新理论。数据还有助于指出何时最有效的减少偏差干预措施。由于该模型提供了一种分别衡量社会类别和个人属性影响的方法，因此可以仔细评估哪些策略最有效地减少偏见以及在什么情况下。该项目还通过开发和共享一种用于衡量和分析行为数据的新颖工具来改善科学基础设施。该奖项反映了NSF的法定使命，并使用基金会的知识分子优点和更广泛的影响评估标准，认为值得通过评估来获得支持。