A model-based test for treatment effects with probabilistic classifications.

ABSTRACT: Within modern psychology, computational and statistical models play an important role in describing a wide variety of human behavior. Model selection analyses are typically used to classify individuals according to the model(s) that best describe their behavior. These classifications are inherently probabilistic, which presents challenges for performing group-level analyses, such as quantifying the effect of an experimental manipulation. We answer this challenge by presenting a method for quantifying treatment effects in terms of distributional changes in model-based (i.e., probabilistic) classifications across treatment conditions. The method uses hierarchical Bayesian mixture modeling to incorporate classification uncertainty at the individual level into the test for a treatment effect a

SUBMITTER: Cavagnaro DR 

PROVIDER: S-EPMC6249127 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature