A Connectionist Model of Category Size Effects During Learning

This paper reports the results of category learning experiments in which the number of exemplars defining a category during learning was varied. These results reveal that category exemplars from larger sized categories are classified more accurately than those from smaller-sized categories. This was true both early and late in learning. In addition, subjects exhibited a response bias toward classifying exemplars into larger-sized categories throughout learning. A connectionist model is developed which exhibits these same tendencies.