APPLYING OCCAM RAZOR IN MODELING COGNITION - A BAYESIAN-APPROACH

In mathematical modeling of cognition, it is important to have well;ju stified criteria for choosing among differing explanations (i.e,, mode ls) of observed data. This paper introduces a Bayesian model selection approach that formalizes Occam's razor, choosing the simplest model t hat describes the data well. The choice of a model is carried out by t aking into account not only the traditional model selection criteria ( i.e., a model's fit to the data and the number of parameters) but also the extension of the parameter space, and, most importantly, the func tional form of the model (i.e., the way in which the parameters are co mbined in the model's equation). An advantage of the approach is that it can be applied to the comparison of non-nested models as well as ne sted ones. Application examples are presented and implications of the results for evaluating models of cognition are discussed.