title: Optimal experimental design for model discrimination.
abstract: Models of a psychological process can be difficult to discriminate experimentally because it is not easy to determine the values of the critical design variables (e.g., presentation schedule, stimulus structure) that will be most informative in differentiating them. Recent developments in sampling-based search methods in statistics make it possible to determine these values and thereby identify an optimal experimental design. After describing the method, it is demonstrated in 2 content areas in cognitive psychology in which models are highly competitive: retention (i.e., forgetting) and categorization. The optimal design is compared with the quality of designs used in the literature. The findings demonstrate that design optimization has the potential to increase the informativeness of the experimental method.
authors.affiliation: Department of Psychology, Ohio State University, Columbus, OH 43210-1351, USA. myung.1@osu.edu
authors.forename: Jay I
authors.initials: JI
authors.lastname: Myung
authors.forename: Mark A
authors.initials: MA
authors.lastname: Pitt
mesh_terms: D000465:Algorithms; D000818:Animals; D001244:Association; D003210:Concept Formation; D004192:Discrimination, Psychological; D006801:Humans; D016013:Likelihood Functions; D008390:Markov Chains; D008960:Models, Psychological; D009010:Monte Carlo Method; D012107:Research Design / Q000706:statistics & numerical data*; D012153:Retention, Psychology; D035501:Uncertainty