title: Bayesian outcome-based strategy classification.
abstract: Hilbig and Moshagen (Psychonomic Bulletin & Review, 21, 1431-1443, 2014) recently developed a method for making inferences about the decision processes people use in multi-attribute forced choice tasks. Their paper makes a number of worthwhile theoretical and methodological contributions. Theoretically, they provide an insightful psychological motivation for a probabilistic extension of the widely-used "weighted additive" (WADD) model, and show how this model, as well as other important models like "take-the-best" (TTB), can and should be expressed in terms of meaningful priors. Methodologically, they develop an inference approach based on the Minimum Description Length (MDL) principles that balances both the goodness-of-fit and complexity of the decision models they consider. This paper aims to preserve these useful contributions, but provide a complementary Bayesian approach with some theoretical and methodological advantages. We develop a simple graphical model, implemented in JAGS, that allows for fully Bayesian inferences about which models people use to make decisions. To demonstrate the Bayesian approach, we apply it to the models and data considered by Hilbig and Moshagen (Psychonomic Bulletin & Review, 21, 1431-1443, 2014), showing how a prior predictive analysis of the models, and posterior inferences about which models people use and the parameter settings at which they use them, can contribute to our understanding of human decision making.
authors.affiliation: Department of Cognitive Sciences, University of California, Irvine, CA, 92697-5100, USA. mdlee@uci.edu.
authors.forename: Michael D
authors.initials: MD
authors.lastname: Lee
mesh_terms: D001499:Bayes Theorem; D002755:Choice Behavior; D003657:Decision Making; D006801:Humans; D007206:Individuality; D008960:Models, Psychological; D011340:Problem Solving
keywords: Bayesian inference; Decision making; Graphical models; Individual differences; Strategy classification