Bayesian modeling approaches for individual differences in social cognition

Research in social cognition often relies on experimental tasks that generate responses in terms of accuracy and response times. Consider, for instance, the Implicit Association Test (IAT), which captures attitudes and stereotypes by measuring the strength of associations between concepts (e.g., race) and evaluations (e.g., good or bad) in a categorization task. In this task, based on cultural stereotypes, we expect responses to be faster and more accurate with white-positive / black-negative pairings than with black-positive / white-negative pairings. In this talk, we will introduce and illustrate different Bayesian hierarchical modeling approaches for the IAT. First, we will attempt to characterize the typical data pattern observed in the IAT, in order to better understand the relationship between speed and accuracy. Second, based on this pattern, we will outline three analytic approaches for quantifying individual differences in implicit associations that constitute alternatives to the traditional D-score analysis of the IAT. Specifically, we apply Bayesian hierarchical multivariate regression, multinomial processing trees with response times, and lognormal race models to the IAT data. These approaches share the benefit of integrating both response time and accuracy data and thus making use of the full resolution of the data. Additionally, the three modeling techniques have unique features that make them more or less suitable depending on the particular research question, theoretical focus, and design characteristics at hand. We will apply each model to two different datasets and discuss advantages, predictions, and individual estimates from each model.