ABSTRACT
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
