SIMULATION-MODELS OF THE INFLUENCE OF LEARNING-MODE AND TRAINING VARIANCE ON CATEGORY LEARNING

This article uses simulation as an empirical method for identifying pr ocess models of strategy effects in a category-learning task. A genera l set of learning assumptions defined a symbolic learning framework in which alternative simulation models were defined and tested. The goal was to identify process models that could account for previously repo rted data on the interaction between how a learner encounters category variance across a series of training samples and whether the task ins tructions suggested an active, hypothesis-testing approach, or a more passive learning mode. Descriptive characterizations of active and pas sive learning were mapped into complementary settings of parameters op erating with the general learning framework. Alternative models, defin ed by different configurations of these parameters, were evaluated on their goodness of fit to the observed data. The signature differences between models that best fit the passive learning data and models that best fit the active learning data concerned a delayed versus immediat e learning parameter and a degree-of-match parameter that determined w hich patterns were retrieved to make category decisions. A functional account of these parameters is given by considering the learning task as a search process and the role of these parameters in localizing the impact of learning mechanisms in certain areas of the search space. I ssues related to simulation as an empirical method for identifying can didate process models are discussed.