Prototypes, exemplars and the response scaling parameter: A Bayes factor perspective

The Bayes factor can be used to break the stalemate between prototype and exemplar theorists in category learning. Exemplar theorists do not accept prototype theorists' results, because these results are often based on a restricted version of the exemplar model, without response scaling parameter. Prototype theorists do not accept exemplar theorists' results, because these results are often based on comparing fits of models that are unbalanced in their number of parameters. Using the Bayes factor alleviates concerns about differences in model complexity between exemplar and prototype models, because it takes away the prototype theorists' fear that their model is disadvantaged, and it eliminates the need to use a constrained version of the exemplar model exemplar theorists have abandoned. Further, by virtue of its sensitivity to the prior, the adoption of the Bayes factor encourages more complete theorizing about the relevant psychological processes instantiated in the models, and increases the empirical content of the models.