Exemplar-memory and adaptive network models were compared in application to category learning data, with special attention to base rate effects on learning and transfer performance. Subjects classified symptom charts of hypothetical patients into disease categories, with informative feedback on learning trials and with the feedback either given or withheld on test trials that followed each fourth of the learning series. The network model proved notably accurate and uniformly superior to the exemplar model in accounting for the detailed course of learning; both the parallel, interactive aspect of the network model and its particular learning algorithm contribute to this superiority. During learning, subjects' performance reflected both category base rates and feature (symptom) probabilities in a nearly optimal manner, a result predicted by both models, though more accurately by the network model. However, under some test conditions, the data showed substantial base-rate neglect, in agreement with Gluck and Bower (1988b).

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This study investigates human motor programming by manipulating stimulusresponse (S-R) compatibility in choice reaction time tasks where subjects choose between sequences of key-pressing responses. In the first experiment we show that S-R compatibility and sequence length have additive effects on choice reaction time. In the second and third experiment we show that choice reaction time is also influenced by the compatibility between the reaction signal and noninitial responses of the required sequence. In all three experiments interresponse times are unaffected by S-R compatibility but do depend on response groupings. The results provide a new source of evidence for parameter setting in motor programming. The model we adopt assumes that abstract parameter values are distributively assigned to hierarchically ordered subprograms for forthcoming responses.

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A theory for cyclic voltammetry at liquid/liquid interfaces for electron transfer is proposed, and the numerical solution of the resulting integral equation is evaluated. This theory has been used to show how the cyclic voltammograms, for electron transfer at ITIES, vary when different ratios of reactants and products, in both phases, are used, and how these voltammograms differ from classical cyclic voltammograms.