title: Bayesian estimation of multinomial processing tree models with heterogeneity in participants and items.
abstract: Multinomial processing tree (MPT) models are theoretically motivated stochastic models for the analysis of categorical data. Here we focus on a crossed-random effects extension of the Bayesian latent-trait pair-clustering MPT model. Our approach assumes that participant and item effects combine additively on the probit scale and postulates (multivariate) normal distributions for the random effects. We provide a WinBUGS implementation of the crossed-random effects pair-clustering model and an application to novel experimental data. The present approach may be adapted to handle other MPT models.
authors.affiliation: Department of Psychology, University of Amsterdam, Weesperplein 4, 1018 XA, Amsterdam, The Netherlands, d.matzke@uva.nl.
authors.forename: Dora
authors.initials: D
authors.lastname: Matzke
authors.forename: Conor V
authors.initials: CV
authors.lastname: Dolan
authors.forename: William H
authors.initials: WH
authors.lastname: Batchelder
authors.forename: Eric-Jan
authors.initials: EJ
authors.lastname: Wagenmakers
mesh_terms: D001499:Bayes Theorem; D003627:Data Interpretation, Statistical; D006801:Humans; D008960:Models, Psychological; D015233:Models, Statistical