Discrimination Nets as Psychological Models*
Work on this paper was supported by a NSF Graduate Fellowship to the first author and by NIMH Grant 13950 to the second. We are grateful to Irving Biederman, Jeffrey Farrar, Janet Kolodner, Christopher Riesbeck. Brian Ross, Daniel Sewell, and Edward Smith for helpful comments. The final product does not necessarily represent their positions on its Content. Parts of this paper were presented at the Annual Stanford‐Berkeley Experimental Psychology Conference, Stanford University, 1980.
Abstract
Simulations of human cognitive processes often employ discrimination nets to model the access of permanent memory. We consider two types of discrimination nets—EPAM and positive‐proper‐only nets—and argue that they have insufficient psychological validity. Their deficiencies arise from negative properties, insufficient sensitivity to the discriminativeness of properties, extreme sensitivity to missing or incorrect properties, inefficiency in representing multiple knowledge domains, and seriality. We argue that these deficiencies stem from a high degree of test contingency in utilizing property information during acquisition and memory search. Discrimination nets are compared to other models that have less or no test contingency (e.g., PANDEMONIUM) and that thereby avoid the problems of discrimination nets. We propose that understanding test contingency and discovering psychologically valid ways to implement it will be central to understanding and simulating memory indexing in human cognition.
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