ABSTRACT
One problem in evaluating recent computational models of human category learning is that there is no standardized method for systematically comparing the models’ assumptions or hypotheses. In the present study, a flexible general model (called GECLE) is introduced that can be used as a framework to systematically manipulate and compare the effects of a limited number of assumptions at a time. Two simulation studies are presented to show how the GECLE framework can be useful in the field of human high-order cognition research.
