ABSTRACT
We test a configural-cue network model of human classification and recognition learning based on Rescorla & Wagner's (1972) model of classical conditioning. The model extends the stimulus representation assumptions from our earlier one-layer network model (Gluck & Bower, 1988b) to include pair-wise conjunctions of features as unique cues. Like the exemplar context model of Medin & Schaffer (1978), the representational assumptions of the configural-cue network model embody an implicit exponential decay relationship between stimulus similarity and and psychological (Hamming) distance, a relationship which has received substantial independent empirical and theoretical support (Shepard, 1957, 1987). In addition to results from animal learning, the model accounts for several aspects of complex human category learning, including the relationship between category similarity and linear separability in determining classification difficulty (Medin & Schwanenflugel, 1981), the relationship between classification and recognition memory for instances (Hayes-Roth & Hayes-Roth, 1977), and the impact of correlated attributes on classification (Medin, Altom, Edelson, & Freko, 1982).
