title: Minimum description length model selection of multinomial processing tree models.
abstract: Multinomial processing tree (MPT) modeling has been widely and successfully applied as a statistical methodology for measuring hypothesized latent cognitive processes in selected experimental paradigms. In this article, we address the problem of selecting the best MPT model from a set of scientifically plausible MPT models, given observed data. We introduce a minimum description length (MDL) based model-selection approach that overcomes the limitations of existing methods such as the G(2)-based likelihood ratio test, the Akaike information criterion, and the Bayesian information criterion. To help ease the computational burden of implementing MDL, we provide a computer program in MATLAB that performs MDL-based model selection for any MPT model, with or without inequality constraints. Finally, we discuss applications of the MDL approach to well-studied MPT models with real data sets collected in two different experimental paradigms: source monitoring and pair clustering. The aforementioned MATLAB program may be downloaded from http://pbr.psychonomic-journals.org/content/supplemental.
authors.affiliation: Department of Psychology, Ohio State University, Columbus, Ohio, USA. wu.498@osu.edu
authors.forename: Hao
authors.initials: H
authors.lastname: Wu
authors.forename: Jay I
authors.initials: JI
authors.lastname: Myung
authors.forename: William H
authors.initials: WH
authors.lastname: Batchelder
mesh_terms: D001288:Attention; D001499:Bayes Theorem; D003071:Cognition; D003625:Data Collection / Q000706:statistics & numerical data*; D006801:Humans; D016013:Likelihood Functions; D008432:Mathematical Computing; D015233:Models, Statistical; D021641:Recognition, Psychology; D012984:Software; D014706:Verbal Learning