Understanding Attitude Associations by Modeling Decision Processes on the Implicit Association Test: A Tutorial on the Tug-of-War Model.

In this tutorial, we describe a newly developed model of Implicit Association Test (IAT) data—the Tug-of-war model—which introduces a theoretically meaningful association parameter and mathematically specified mechanism through which the parameter explains the IAT data. We outline the model's benefits over existing alternatives, estimate its parameters for a large data set (N > 10,000), and describe inferences that can be drawn from it. Additionally, we provide an in-depth description of the model structure, its parameters, and a step-by-step guide to fit the model to IAT data. To minimize any technical learning curve, we provide detailed guidelines for all required computational tools (e.g., JAGS) needed to implement our model. We hope this tutorial makes it easier to use our model for IAT data and to further enable social cognition researchers to explain behavior using mathematical models. [ABSTRACT FROM AUTHOR]