Much recent evidence suggests some dramatic differences in the way people learn perceptual categories, depending on exactly how the categories were constructed. Four different kinds of category-learning tasks are currently popular-rule-based tasks, information-integration tasks, prototype distortion tasks, and the weather prediction task. The cognitive, neuropsychological, and neuroimaging results obtained using these four tasks are qualitatively different. Success in rule-based (explicit reasoning) tasks depends on frontal-striatal circuits and requires working memory and executive attention. Success in information-integration tasks requires a form of procedural learning and is sensitive to the nature and timing of feedback. Prototype distortion tasks induce perceptual (visual cortical) learning. A variety of different strategies can lead to success in the weather prediction task. Collectively, results from these four tasks provide strong evidence that human category learning is mediated by multiple, qualitatively distinct systems.

The performance of a decision bound model of categorization (Ashby, 1992a; Ashby & Maddox, in press) is compared with the performance of two exemplar models. The first is the generalized context model (e.g., Nosofsky, 1986Nosofsky, , 1992 and the second is a recently proposed deterministic exemplar model (Ashby & Maddox, in press), which contains the generalized context model as a special case. When the exemplars from each category were normally distributed and the optimal decision bound was linear, the deterministic exemplar model and the decision bound model provided roughly equivalent accounts of the data. When the optimal decision bound was nonlinear, the decision bound model provided a more accurate account of the data than did either exemplar model. When applied to categorization data collected by Nosofsky (1986Nosofsky ( ,1989, in which the category exemplars are not normally distributed, the decision bound model provided excellent accounts of the data, in many cases significantly outperforming the exemplar models. The decision bound model was found to be especially successful when (1) single subject analyses were performed, (2) each subject was given relatively extensive training, and (3) the subject's performance was characterized by complex suboptimalities. These results support the hypothesis that the decision bound is of fundamental importance in predicting asymptotic categorization performance and that the decision bound models provide a viable alternative to the currently popular exemplar models of categorization.Decision bound models of categorization (Ashby, 1992a; Ashby & Maddox, in press) assume that the subject learns to assign responses to different regions of perceptual space. When categorizing an object, the subject determines in which region the percept has fallen and then emits the associated response. The decision bound is the partition between competing response regions. In contrast, exemplar models assume that the subject computes the sum of the perceived similarities between the object to be categorized and every exemplar of each relevant category (Medin & Schaffer, 1978;Nosofsky, 1986). Categorization judgments are assumed to depend on the relative magnitude of these various sums.This article compares the ability of decision bound and exemplar models to account for categorization response probabilities in seven different experiments. The aim is

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The effect of immediate versus delayed feedback on rule-based and information-integration category learning was investigated. Accuracy rates were examined to isolate global performance deficits, and model-based analyses were performed to identify the types of response strategies used by observers. Feedback delay had no effect on the accuracy of responding or on the distribution of best fitting models in the rule-based category-learning task. However, delayed feedback led to less accurate responding in the information-integration category-learning task. Model-based analyses indicated that the decline in accuracy with delayed feedback was due to an increase in the use of rule-based strategies to solve the information-integration task. These results provide support for a multiple-systems approach to category learning and argue against the validity of single-system approaches.

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Humans live in a world of categories, rather than unique instances. Categories divide the world into meaningful pieces. Humans categorize in order to reach cognitive economy of memory, to communicate and understand, and to explain and predict properties and actions of new stimuli on the basis of older experiences. Because categorization is essential for higher level cognition, much attention in cognitive research has been paid to category learning (see, e.g., Ashby & Maddox, 2005;Estes, 1994;Kruschke, 1992;Love, Medin, & Gureckis, 2004;Medin & Schaffer, 1978;Nosofsky, 1986).A large and growing body of research suggests that participants have available multiple processing modes that can be used during categorization. Well established in the literature is a distinction between categorization according to a rule and categorization based on overall similarity (Allen & Brooks, 1991;Erickson & Kruschke, 1998;Folstein & Van Petten, 2004;Kemler Nelson, 1984;Nosofsky, Palmeri, & McKinley, 1994;Regehr & Brooks, 1993). Building upon this work on multiple processing modes is a recent interest in understanding the neurobiological underpinnings of category learning and examining the possibility of multiple systems of category learning (Ashby, Alfonso-Reese, Turken, & Waldron, 1998;Poldrack, Prabhakaran, Seger, & Gabrieli, 1999;Reber, Stark, & Squire, 1998;E. E. Smith, Patalano, & Jonides, 1998; for reviews, see Kéri, 2003, and. Relevant to this work are studies of multiple memory systems (Poldrack & Packard, 2003; Schacter & Tulving, 1994;Squire, 1992) and multiple reasoning systems (Sloman, 1996).One multiple systems model of perceptual category learning, and the only one that specifies the underlying neurobiology, is the competition between verbal and implicit systems (COVIS) model proposed by Ashby et al. (1998;Ashby & Waldron, 2000). COVIS postulates two systems that compete throughout learning: an explicit hypothesis-testing system, which uses logical reasoning and depends on working memory and executive attention, and an implicit procedural-learning-based system. (Relations between COVIS and the multiple process [rule vs. overall similarity] approach are reserved for the General Discussion section.)At the implementation level, the explicit hypothesistesting and the implicit procedural-learning systems have distinct but partially overlapping neurobiological underpinnings. The key neural structures for the hypothesistesting system are the prefrontal cortex, the anterior cingulate, and the head of the caudate nucleus. The key neural structures for the procedural-learning system are the inferotemporal cortex and the tail of the caudate nucleus. A dopamine-mediated reward signal from the substantia nigra is critical for learning in this system. Both systems attempt to acquire and solve every categorization task encountered. However, the relative weight of each system in the category judgment depends on the relative success of each system in category learning, which, in turn, depends on the type of category structure to be ac...

During the 1990’s and early 2000’s, cognitive neuroscience investigations of human category learning focused on the primary goal of showing that humans have multiple category learning systems and on the secondary goals of identifying key qualitative properties of each system and of roughly mapping out the neural networks that mediate each system. Many researchers now accept the strength of the evidence supporting multiple systems, and as a result, during the past few years, work has begun on the second generation of research questions – that is, on questions that begin with the assumption that humans have multiple category learning systems. This article reviews much of this second generation of research. Topics covered include: 1) How do the various systems interact? 2) Are there different neural systems for categorization and category representation? 3) How does automaticity develop in each system?, and 4) Exactly how does each system learn?

The effects of two different kinds of categorizationtraining were investigated. In observational training, observersare presented with a category label and then shown an exemplar from that category. In feedback training, they are shown an exemplar, asked to assign it to a category, and then given feedback about the accuracy of their response. These two types of training were compared as observers learned two types of category structures-those in which optimal accuracy could be achieved via some explicit rule-based strategy, and those in which optimal accuracy required integrating information from separate perceptual dimensions at some predecisional stage. There was an overall advantage for feedback training over observational training, but most importantly, type of training interactedstrongly with type of category structure. With rule-based structures, the effects of training type were small, but with information-integrationstructures, accuracy was substantially higher with feedback training, and people were less likely to use suboptimal rule-based strategies. The implications of these results for current theories of category learning are discussed.