Learning and using specific instances.

A biologically plausible method for rapidly learning specific instances is described. It is contrasted with a formal model of classical conditioning (Rescorla-Wagner learning/perception training), which is shown to be relatively good for learning generalizations, but correspondingly poor for learning specific instances. A number of behaviorally relevant applications of specific instance learning are considered. For category learning, various combinations of specific instance learning and generalization are described and analyzed. Two general approaches are considered: the simple inclusion of Specific Instance Detectors (SIDs) as additional features during perception training, and specialized treatment in which SID-based categorization takes precedence over generalization-based categorization. Using the first approach, analysis and empirical results demonstrate a potential problem in representing feature presence and absence in a symmetric fashion when the frequencies of feature presence and absence are very different. However, it is shown that by using the proper representation, the addition of SIDs can only improve the convergence rate of perceptron training, the greatest improvement being achieved when SIDs are preferentially allocated for peripheral positive and negative instances. Some further improvement is possible if SIDs are treated in a specialized manner.