haracterized by descriptions of each module and how each serves in those tasks for which it is best suited. However, these theories often do not emphasize how modules interact in producing responses and in learning. In this article we will develop a modular theory of categorization that follows from two distinct accounts of this behavior. The first account is that of rule-based theories of categorization. These theories emerge from a philosophical tradition in which concepts and categorization are described in terms of definitional rules. For example, if a living thing has a wide, flat tail and constructs dams by cutting down trees with its This work was supported by Indiana University Cognitive Science Program Fellowships and by NIMH ResearchTraining Grant PHS-T32-MH19879-03 to Erickson, and in part by NIMH FIRST Award 1-R29-MH51572-01 to Kruschke. This research was reported as a poster at the 1996 Cognitive Science Society Conference in San Diego, CA. We than

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this article should be addressed to W. Todd Maddox, Department of Psychology, Mezes Hall 330 Mail Code B3800, University of Texas, Austin, Texas, 78712. E-mail: maddox@psy.utexas.edu

Speeded perceptual classification experiments were conducted to distinguish among the predictions of exemplar-retrieval, decision-boundary, and prototype models. The key manipulation was that across conditions, individual stimuli received either probabilistic or deterministic category feedback. Regardless of the probabilistic feedback, however, an ideal observer would always classify the stimuli by using an identical linear decision boundary. Subjects classified the probabilistic stimuli with lower accuracy and longer response times than they classified the deterministic stimuli. These results are in accord with the predictions of the exemplar model and challenge the predictions of the prototype and decision-boundary models. A fundamental issue in the field of perceptual classification concerns the manner in which people represent categories in memory and the decision processes that they use for making classification judgments. Among the major formal models of perceptual classification are exemplar-retrieval, prototype, and decision-boundary models. According to exemplar-retrieval models (Hintzman, 1986; Medin & Schaffer, 1978; Nosofsky, 1986), people represent categories by storing individual exemplars of categories in memory, and they make classification decisions on the basis of the similarity of test items to these stored exemplars. According to prototype models (Posner & Keele, 1968; Reed,

The authors present 2 experiments that establish the presence of knowledge partitioning in perceptual categorization. Many participants learned to rely on a context cue, which did not predict category membership but identified partial boundaries, to gate independent partial categorization strategies. When participants partitioned their knowledge, a strategy used in 1 context was unaffected by knowledge demonstrably present in other contexts. An exemplar model, attentional learning covering map, was shown to be unable to accommodate knowledge partitioning. Instead, a mixture-of-experts model, attention to rules and instances in a unified model (ATRIUM), could handle the results. The success of ATRIUM resulted from its assumption that people memorize not only exemplars but also the way in which they are to be classified. In this article, we address the representation of complex perceptual categories. Contrary to the conventional and widespread assumption that people’s representations are homogeneous and integrated, we show in two experiments that people often master a complex categorization task by forming independent components, or parcels, of knowledge. We also show that once a knowledge

Experiments were conducted to contrast the predictions from exemplar models and rulebased decision-bound models of perceptual classification. Observers classified multidimensional stimuli into categories that could be described in terms of easily verbalizable logical rules. The critical manipulation was that some pairs of stimuli received probabilistic feedback, whereas other control pairs received deterministic feedback. Despite the probabilistic feedback, the probabilistic pairs and the deterministic pairs were the same distance from idealobserver, rule-based decision boundaries. Across two experiments with varying category structures, observers classified the probabilistic pairs with slower response times (RTs) and lower accuracies than the comparison deterministic pairs. The effects were relatively long-term, extending into test blocks in which all feedback was withheld. The results were as predicted by exemplar models, but challenged models that posit that RT is a function solely of the distance of a stimulus from rule-based boundaries. The studies add considerable generality to previous ones and suggest that, even in domains involving rule-based category structures, exemplarretrieval processes play a significant role. Supplementary material related to this article may be downloaded from www.psychonomic.org/archive.

The new field of developmental robotics faces the formidable challenge of implementing effective learning mechanisms in complex, dynamic environments. We make a case that knowledge-based learning algorithms might help to meet this challenge. A constructive neural learning algorithm, knowledge-based cascade-correlation (KBCC), autonomously recruits previously-learned networks in addition to the single hidden units recruited by ordinary cascade-correlation. This enables learning by analogy when adequate prior knowledge is available, learning by induction from examples when there is no relevant prior knowledge, and various combinations of analogy and induction. A review of experiments with KBCC indicates that recruitment of relevant existing knowledge typically speeds learning and sometimes enables learning of otherwise impossible problems. Some additional domains of interest to developmental robotics are identified in which knowledge-based learning seems essential. The characteristics of KBCC in relation to other knowledge-based neural learners and analogical reasoning are summarized as is the neurological basis for learning from knowledge. Current limitations of this approach and directions for future work are discussed. Keywords: Knowledge-based learning; neural networks; knowledge transfer; developmental robotics. 245 246 T. R. Shultz et al. 1.