Applying Occam's razor in modeling cognition: A Bayesian approach

In mathematical modeling of cognition, it is important to have well;justified criteria for choosing among differing explanations (i.e,, models) of observed data. This paper introduces a Bayesian model selection approach that formalizes Occam's razor, choosing the simplest model that describes the data well. The choice of a model is carried out by taking 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 functional form of the model (i.e., the way in which the parameters are combined 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 nested ones. Application examples are presented and implications of the results for evaluating models of cognition are discussed.