ISSN : 1226-9654
Two experiments were conducted to contrast quantitatively the MDS-based exemplar model (GCM) and the prototype abstraction model in predicting classification and old-new recognition performance of ill-defined category instances. Both models employed the typical learning-transfer phase paradigm to collect classification and recognition data. Experiment 1 used random dot patterns as instances and increased the category size. (up to 18 instances/ category) to extend the possibility of prototype abstraction. The MDS-based GCM and Prototype model were fitted to the data. The overall results of model-based theoretical analyses showed no indication of prototype abstraction. The results were interpreted that category size makes no contribution to prototype abstaction in the case of random dot patterns. Experiment 2 used Reed's(1972) schematic faces as category instances that are more meaningful compared to the random dot patterns. The results of theoretical analyses showed that abstracted prototype can contribute to the classification of schematic faces into categories. Category representation, the problem of similarity measurement, and alternative models for classification process were discussed.