Intersectionality and Implicit Bias

Connor, Paul Robert, San Francisco CA

Investigators

Abstract

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). This award was provided as part of NSF's Social, Behavioral and Economic Sciences Postdoctoral Research Fellowships (SPRF) program. The goal of the SPRF program is to prepare promising, early career doctoral-level scientists for scientific careers in academia, industry or private sector, and government. SPRF awards involve two years of training under the sponsorship of established scientists and encourage Postdoctoral Fellows to perform independent research. NSF seeks to promote the participation of scientists from all segments of the scientific community, including those from underrepresented groups, in its research programs and activities; the postdoctoral period is considered to be an important level of professional development in attaining this goal. Each Postdoctoral Fellow must address important scientific questions that advance their respective disciplinary fields. Under the sponsorship of Dr. Jonathan Freeman at New York University, this postdoctoral fellowship award supports an early career scientist investigating implicit evaluations of multiply categorizable social targets. A large scientific literature suggests that individuals possess implicit evaluative biases with regard to a range of social categories (e.g., automatically associate social categories such as ‘Black’ or ‘White’ with negative or positive valence), yet little is known about how implicit evaluations respond when multiple social categories are perceived simultaneously within the same social target (e.g., when an onlooker perceives an old Black female or a young White male). This is an important gap in our knowledge, because in the vast majority of human interactions, individuals are multiply categorizable. Therefore, understanding how implicit evaluative biases respond to multiply categorizable social targets is likely vital to understanding how those biases shape and affect individuals’ lives, and to the extent that understanding is missing, efforts to identify and ameliorate the social impacts of implicit bias may be limited. The present research is aimed at building that understanding from the ground up, and gaining a firm evidentiary foothold regarding the underlying cognitive processes involved. This knowledge will be vital toward being able to accurately predict where, when, to what extent, and with respect to whom implicit evaluative bias is most likely to manifest, all questions that are crucial toward the development of interventions aimed at identifying and reducing implicit bias, and to creating institutions and systems that can effectively counteract and nullify its harmful effects. The present project will adopt a novel approach to understanding how implicit evaluations operate in the presence of multiply categorizable social targets. Using an associative learning paradigm previously employed in Professor Freeman’s lab, the present research will seek to create implicit biases ‘from thin air,’ by training participants to associate multiple novel social categories with pro- or anti-social behaviors. Following this, the researchers will observe how implicit evaluative biases are displayed toward novel targets simultaneously displaying each of the behavior-associated categories (likely via Evaluative Priming Tasks, as in previous research conducted in Professor Freeman’s lab). By doing so, the present project will document, for the first time, the basic cognitive processes by which perceivers combine separate implicit evaluative biases in the presence of multiply categorizable social targets. Following this initial step, the present research will seek to investigate a number of potential moderating factors governing these core underlying processes via a series of experiments, including the visual salience of categories, perceivers’ attention to categories, and perceivers’ relative levels of bias with respect to categories. 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.