A neuropsychological theory is proposed that assumes category learning is a competition between separate verbal and implicit (i.e., procedural-learning-based) categorization systems. The theory assumes that the caudate nucleus is an important component of the implicit system and that the anterior cingulate and prefrontal cortices are critical to the verbal system. In addition to making predictions for normal human adults, the theory makes specific predictions for children, elderly people, and patients suffering from Parkinson's disease, Huntington's disease, major depression, amnesia, or lesions of the prefrontal cortex. Two separate formal descriptions of the theory are also provided. One describes trial-by-trial learning, and the other describes global dynamics. The theory is tested on published neuropsychological data and on category learning data with normal adults.

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Observers were tested in a perceptual category-learning experiment in which they were instructed to make classification decisions as rapidly as possible without making errors. Nosofsky and Palmeri's (1997b) exemplar-based random walk (EBRW) model of speeded classification was tested for its ability to fit the classification response times and accuracies. The authors demonstrated that the EBRW model provided good quantitative fits to the mean response times and accuracies associated with individual objects as a function of their locations in a multidimensional similarity space and as a function of practice in the task. Preliminary evidence was also obtained that stimulus-specific adjustments in the random walk response criteria may have occurred during the course of learning.Numerous powerful models of multidimensional perceptual classification exist in the field today, providing detailed quantitative accounts of category learning, performance, and generalization (e.g., Anderson, 1991;Ashby & Lee, 1991;Estes, 1994;Gluck & Bower, 1988;Hintzman, 1986;Medin & Schaffer, 1978;Nosofsky, 1986). However, most of these models are limited to predicting the output of classification, such as choice probabilities, confidence ratings, typicality judgments, and so forth. It is only recently that investigators have attempted to extend these models to account for the actual time course of categorization decision making and to predict classification response times (e.g., Ashby, Boynton, & Lee, 1994;Lamberts, 1995Lamberts, , 1998Maddox & Ashby, 1996; Nosofsky & Palmeri, 1997a, 1997b. Because response times provide a window into understanding the nature of people's category representations and decision processes, it is important to extend current models to account for this form of data.The purpose of this research was to provide further tests of a recently proposed exemplar-retrieval model for predicting the time course of classification decision making. Nosofsky and Palmeri's (1997b) exemplarbased random walk (EBRW) model is an integrated model that combines key elements of Nosofsky's (1986) generalized context model of perceptual classification and Logan's (1988) instance theory ofautomaticity. According to the EBRW, people represent categories by storing individ-

To understand why some categorization tasks are more difficult than others, we consider five factors that may affect human performance-namely, covariance complexity, optimal accuracy level with and without internal noise, orientation of the optimal categorization rule, and class separability. We argue that covariance complexity, an information-theoretic measure of complexity, is an excellent predictor of task difficulty. We present an experiment that consists of five conditions using a simulated medical decision-making task. In the task human observers view hundreds of hypothetical patient profiles and classify each profile into Disease Category A or B. Each profile is a continuous-valued, three-dimensional stimulus consisting of three vertical bars, where each bar height represents the result of a medical test. Across the five conditions, covariance complexity was systematically manipulated. Results indicate that variation in performance is largely a function of covariance complexity and partly a function of internal noise. The remaining three factors do not explain performance results. We present a challenge to categorization theorists to design models that account for human performance as predicted by covariance complexity.