A fundamental question is whether people have available one category learning system, or many. Most multiple systems advocates postulate one explicit and one implicit system. Although there is much agreement about the nature of the explicit system, there is less agreement about the nature of the implicit system. In this article, we review a dual systems theory of category learning called competition between verbal and implicit systems (COVIS) developed by Ashby et al. (1998). The explicit system dominates the learning of verbalizable, rule-based category structures and is mediated by frontal brain areas such as the anterior cingulate, prefrontal cortex (PFC), and head of the caudate nucleus. The implicit system, which uses procedural learning, dominates the learning of non-verbalizable, information-integration category structures, and is mediated by the tail of the caudate nucleus and a dopamine-mediated reward signal. We review nine studies that test six a priori predictions from COVIS, each of which is supported by the data.

This review proposes that implicit learning processes are the cognitive substrate of social intuition. This hypothesis is supported by (a) the conceptual correspondence between implicit learning and social intuition (nonverbal communication) and (b) a review of relevant neuropsychological (Huntington's and Parkinson's disease), neuroimaging, neurophysiological, and neuroanatomical data. It is concluded that the caudate and putamen, in the basal ganglia, are central components of both intuition and implicit learning, supporting the proposed relationship. Parallel, but distinct, processes of judgment and action are demonstrated at each of the social, cognitive, and neural levels of analysis. Additionally, explicit attempts to learn a sequence can interfere with implicit learning. The possible relevance of the computations of the basal ganglia to emotional appraisal, automatic evaluation, script processing, and decision making are discussed. These "feelings " have an efficiency of operation which it is impossi-ble for thought to match. Even our most highly intellectualized operations depend upon them as a "fringe " by which to guide our inferential movements. They give us our sense of rightness and wrongness, of what to select and emphasize and follow up, and what

predictions of exemplar models and that supported prototype models. In the authors ’ view, this evidence confounded the issue of the nature of the category representation with the type of response rule (probabilistic vs. deterministic) that was used. Also, their designs did not test whether the prototype models correctly predicted generalization performance. The present work demonstrates that an exemplar model that includes a response-scaling mechanism provides a natural account of all of Smith et al.’s experimental results. Furthermore, the exemplar model predicts classification performance better than the prototype models when novel transfer stimuli are included in the experimental designs. A classic issue in cognitive psychology concerns the manner in which people represent categories in memory. According to prototype models (Homa, 1984; Posner & Keele, 1968; Reed, 1972), people represent categories by forming a summary representation that is a central tendency of all of the experienced members of a

The contribution of the striatum to category learning was examined by having patients with Parkinson's disease (PD) and matched controls solve categorization problems in which the optimal rule was linear or nonlinear using the perceptual categorization task. Traditional accuracy-based analyses, as well as quantitative model-based analyses were performed. Unlike accuracy-based analyses, the model-based analyses allow one to quantify and separate the effects of categorization rule learning from variability in the trial-by-trial application of the participant's rule. When the categorization rule was linear, PD patients showed no accuracy, categorization rule learning, or rule application variability deficits. Categorization accuracy for the PD patients was associated with their performance on a test believed to be sensitive to frontal lobe functioning. In contrast, when the categorization rule was nonlinear, the PD patients showed accuracy, categorization rule learning, and rule application variability deficits. Furthermore, categorization accuracy was not associated with performance on the test of frontal lobe functioning. Implications for neuropsychological theories of categorization learning are discussed. (JINS, 2001, 7, 710 --727.) Keywords: Categorization, Parkinson's disease, Striatum, Memory, Learning

Categorization is a critical skill that every organism must possess in at least a rudimentary form, because it allows organisms to respond differently, for example, to nutrients and poisons and to predators and prey. There is much recent

General recognition theory (GRT) is a multivariate generalization of signal detection theory. Past versions of GRT were static and lacked a process interpretation. This article presents a stochastic version of GRT that models moment-by-moment fluctuations in the output of perceptual channels via a multivariate diffusion process. A decision stage then computes a linear or quadratic function of the outputs from the perceptual channels, which drives a univariate diffusion process that determines the subject's response. Conditions are established under which the stochastic and static versions of GRT make identical accuracy predictions. These equivalence relations show that traditional estimates of perceptual noise may often be corrupted by decisional influences. 2000 Academic Press General recognition theory (GRT), which was first introduced by Ashby and Townsend (1986), is a multivariate generalization of signal detection theory (e.g.,

rule-based but not information-integration

Human concept learning presents a version of the classic problem of induction, which is made particularly difficult by the combination of two requirements: the need to learn from a rich (i.e. nested and overlapping) vocabulary of possible concepts and the need to be able to generalize concepts reasonably from only a few positive examples. I begin this thesis by considering a simple number concept game as a concrete illustration of this ability. On this task, human learners can with reasonable confidence lock in on one out of a billion billion billion logically possible concepts, after seeing only four positive examples of the concept, and can generalize informatively after seeing just a single example. Neither of the two classic approaches to inductive inference -- hypothesis testing in a constrained space of possible rules and computing similarity to the observed examples -- can provide a complete picture of how people generalize concepts in even this simple setting. This thesis prop...

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Early theories of categorization assumed that either rules, or prototypes, or exemplars were exclusively used to mentally represent categories of objects. More recently, hybrid theories of categorization have been proposed that variously combine these different forms of category representation. Our research addressed the question of whether there are representational shifts during category learning. We report a series of experiments that tracked how individual subjects generalized their acquired category knowledge to classifying new critical transfer items as a function of learning. Individual differences were observed in the generalization patterns exhibited by subjects, and those generalizations changed systematically with experience. Early in learning, subjects generalized on the basis of single diagnostic dimensions, consistent with the use of simple categorization rules. Later in learning, subjects generalized in a manner consistent with the use of similarity-based exemplar retrieval, attending to multiple stimulus dimensions. Theoretical modeling was used to formally corroborate these empirical observations by comparing fits of rule, prototype, and exemplar models to the observed categorization data. Although we provide strong evidence for shifts in the kind of information used to classify objects as a function of categorization experience, interpreting these results in terms of shifts in representational systems underlying perceptual categorization is a far thornier issue. We provide a discussion of the