A simple and flexible Bayesian method for inferring step changes in cognition

Human behavioral data often show patterns of sudden change over time. Sometimes the causes of these step changes are internal, such as learning curves changing abruptly when a learner implements a new rule. Sometimes the cause is external, such as people's opinions about a topic changing in response to a new relevant event. Detecting change points in sequences of binary data is a basic statistical problem with many existing solutions, but these solutions rarely seem to be used in psychological modeling. We develop a simple and flexible Bayesian approach to modeling step changes in cognition, implemented as a graphical model in JAGS. The model is able to infer how many change points are justified by the data, as well as the locations of the change points. The basic model is also easily extended to include latent-mixture and hierarchical structures, allowing it to be tailored to specific cognitive modeling problems. We demonstrate the adequacy of this basic model by applying it to the classic Lindisfarne scribes problem, and the flexibility of the modeling approach is demonstrated through two new applications. The first involves a latent-mixture model to determine whether individuals learn categories incrementally or in discrete stages. The second involves a hierarchical model of crowd-sourced predictions about the winner of the US National Football League's Most Valuable Player for the 2016-2017 season.