A Model of Category Learning with Attention Augmented Simplistic Prototype Representation

Abstract

Cognitive models has been a main tool for quantitatively testing theories on human cognition. The results of previous cognitive modeling research collectively suggest the comparative advantage of exemplar over prototype accounts in human cognition. However, we hypothesized that unsuccessful outcomes by traditional prototype models may be the unforeseen consequences of the algorithmic constraints imposed on the models, but not of the implausibility of the theory itself. To test this hypothesis, a new cognitive model based on prototype theory with a more complex and realistic attention system is introduced and evaluated in the present study. A simulation study shows that a new model termed CASPRE resulted in a substantial improvement as compared with the traditional prototype model in replicating empirical findings and that it performed marginally better than an exemplar model, thus confirming our hypothesis.