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

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.