President's Showcase

Martha Cooper

Poster Presentation, Ballroom D
Using Motivational Dimensions to Predict Explicit Racial Bias
Supervising Professor: Dr. Irmak Olcaysoy
Martha Cooper is a senior double majoring in Psychology and Philosophy and aims to study at the intersection of these two fields at the graduate level. Her time as a research assistant and lab manager in a social cognition laboratory has shaped her interests in belief formation and person-perception. She is passionate about improving public understanding of science, critical reasoning, and how we refine knowledge about the self and others. She would like to thank her faculty mentor Dr. Irmak Okten for equipping her with the necessary skills and confidence to complete an independent research project.


In recent years, explicit bias has taken the forefront of conversations surrounding social justice efforts, especially in the context of racial discrimination. Yet, while many acknowledge bias as being undesirable, most people hold some form of bias against members of minority groups (Charlesworth et al., 2021). Unfortunately, negative mental associations can lead to discriminatory practices with far reaching consequences. For example, racial biases can predict the speed at which someone will shoot an individual holding a weapon (Correll et. al., 2002). In addition, higher rates of explicit racial bias in K-12 schools can predict harsher punishments for Black students compared to White students who commit the same transgressions (Riddle & Sinclair, 2018). While research in Social Psychology has sought to identify and resolve the sources of bias, serious racial disparities in healthcare, education, and the criminal justice system persist (Williams & Mohammed, 2008; Quintana & Mahgoub, 2016; Kovera, 2019). My project offers a new perspective on racial bias by examining the role of motivational dimensions in bias reduction efforts. In my study, I developed a new measure tapping into bias regulation motives, which consists of ten different scenarios where racial bias is likely to occur (according to past research). Then, I confirmed that an individual’s perceived value and self-efficacy expectancy can predict their scores on measures of explicit bias. This approach offers evidence that targeting individuals’ bias regulation motives can aid in reducing their levels of racial bias.

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