Research
The McGill Intergroup Cognition Lab is broadly interested in how explicit and implicit intergroup bias can be best conceptualized, measured, and reduced. Ongoing lines of research in the lab include:
1) Identifying and reducing the influence of social information in judgment
How can we know when social information biases behavior? We have sought to make progress on this question by developing a new measure, The Judgment Bias Task (Axt, Nguyen & Nosek, 2018). The JBT is a flexible and reliable method for assessing individual differences in social judgment biases (more information about the JBT can be found in the Resources page). Since its development, we’ve used the JBT to investigate ongoing issues concerning how biases in social judgment can best be reduced (Axt, Yang & Deshpande, 2023, Roy et al., in press). We have also used the task to investigate the mechanisms that allow for discriminatory behavior to arise and persist (Axt & Johnson, 2021; Roy, Dong, Otto & Axt, 2024).
2) Understanding implicit and explicit attitudes
How are our attitudes shaped by our identities and surroundings? And how do these attitudes relate to other psychological processes? In another line of research, we explore questions such as 1) how group status and culture impact implicit attitudes (Axt, Moran & Bar-Anan, 2018; Axt, Atwood, Talhelm & Hehman, 2022), 2) whether such attitudes change over time (Stern & Axt, 2022), 3) how well measures of implicit and explicit attitudes predict relevant beliefs and behavior (Axt, Bar-Anan & Vianello, 2019; Axt, Conway, Buttrick & Westgate, 2021; Buttrick, Axt, Ebersole & Huband, 2020), and 4) the degree to which measures of implicit attitudes create similar or different conclusions (Axt, Buttrick & Feng, 2024).
3) Creating advances in measurement and analysis
All researchers benefit from innovations in methodologiess and improvements in measurement. Past efforts from the lab on this issue have developed new measures (Axt, Nguyen & Nosek, 2018) or applied existing measures to novel contexts (Atwood & Atwood, 2021). We have also used large samples to better understand how to measure implicit and explicit attitudes (Axt, 2018; Hester, Siemers, Axt & Hehman, 2022) or to understand the structure of implicit and explicit attitudes (Axt & Roy, in press).