A parallel rule activation and rule synthesis model for generalization in category learning.

This paper proposes a distinction between primary generalization (transfer from stored exemplars to perceived targets) and secondary generalization (transfer from inferred abstractions to perceived targets). This distinction is embodied in the parallel rule activation and rule synthesis (PRAS) model, a production model capable of exemplar-based and abstraction-based categorization. As an exemplar model, the PRAS model is related to the generalized context model (Nosofsky, 1984). Exemplars are stored in memory encoded as condition-action rules. Working as an exemplar-based model, rules are activated on the basis of their strength and their similarity to the current to-be-categorized instance. Similarity between a target and a stored exemplar is weighted for attention to the dimensions of the psychological space. Depending on the value of a special parameter, the PRAS model is also able to operate as an abstraction model. In the latter case, it attempts to construct generalizing productions, which are activated according to the same rules as the exemplar-specific rules. The model is described in detail. It is applied to a number of important observations described in the research literature, and an experiment is reported that tested the usefulness of the proposed secondary-generalization mechanism. Finally, the discussion elaborates on the implications of the present study for further research.