Stimulus Generalization and Representation in Adaptive Network Models of Category Learning
展示了在人类学习的“构型线索”网络模型中，刺激泛化概率与心理距离之间的近似指数衰减关系如何从刺激表征假设中自然产生，该模型结合了Shepard的理论和Rescorla-Wagner的经典条件反射学习规则。
An exponential-decay relationship between the probability of generalization and psychological distance has received considerable support from studies of stimulus generalization ( Shepard, 1958 ) and categorization ( Nosofsky, 1984 ). It is shown here how an approximate exponential generalization gradient emerges from stimulus representation assumptions isomorphic to a special case of Shepard's (1987) theory of stimulus generalization in a “configuralcue” network model of human learning that represents stimulus patterns in terms of elementary features and pairwise conjunctions of features ( Gluck & Bower, 1988b ; Gluck, Bower, & Hee, 1989 ). The network model can be viewed as a combination of Shepard's theory and an associative learning rule derived from Rescorla and Wagner's (1972) theory of classical conditioning.