Factorial Modeling: A Method for Enhancing the Explanatory and Predictive Power of Cognitive Models

The construction and evaluation of cognitive models can, and often do, lead to novel insights into what might constitute a valid account for an empirical phenomenon. These insights constrain the space of viable models, and could be useful also on a theoretical plane, by promoting a deeper understanding of the studied phenomenon. We propose the factorial method for deriving novel, that is, not theory–based constraints in a principled way during model development. The method is based on a systematic comparison of alternative models, realized through a cross–combination of model components in a generic cognitive model. We illustrate the method by describing an application in the area of mental imagery. We conclude by discussing ways to increase the generalizability of results that can be obtained using the factorial method.