The source and character of graded performance in a symbolic, rule-based model

This paper presents ongoing work that demonstrates how a discrete rule-based model may appropriately manifest graded performance and investigates the source contributing to graded performance of a particular rule-based model called SCA. Previous results have demonstrated that SCA produces appropriate graded performance as a function of learning experience, instance typicality, and other similarity-dependent properties. However, the source of its graded behavior has been somewhat obscured by the presence of continuous components in some aspects of the model. Fully symbolic alternates are presented here and the qualitative predictions from previous work is replicated, thereby suggesting that explicit gradient representations are not necessary for producing graded behavior. In addition to replicating previous results, the results presented here clarify a peculiar character of the model, namely, that the model's typicality differences disappear after extended learning.