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.

人们根据他们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。当社会分类与群体陈规定型观念和偏见联系在一起时，往往会产生负面影响。它可以影响人们思考、判断和对待他人的方式。它大大助长了歧视、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个人感知方面取得了许多进展。这一领域的一个挑战是，很难衡量社会类别与其他类型的个人信息对判断和行为的影响程度。重要的是要更好地区分这些影响力的来源，以更充分地理解群体偏见的起源。本项目开发了一个模型来更直接地测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异的社会偏见的本质。本项目通过开发和应用多项加工树（MPT）模型来更直接地测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。该模型被用来测试特定的假设条件，增加或减少社会分类和定型化的程度。人的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。该模型确定了在何种程度上增加偏见在这些情况下反映了更多的使用定型观念，减少使用个人特征，或两者兼而有之。这些数据被用来评估现有的理论的人的知觉和发展新的理论。这些数据还有助于表明不同的偏倚减少干预措施何时可能最有效。由于该模型提供了一种分别衡量社会类别和个人属性的影响的方法，因此可以仔细评估哪些策略对减少偏见最有效，以及在什么情况下。该项目还通过开发和分享一种测量和分析行为数据的新工具来改善科学基础设施。该奖项反映了NSF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。