This article presents a theory of categorization that accounts for the effects of causal knowledge that relates the features of categories. According to causal-model theory, people explicitly represent the probabilistic causal mechanisms that link category features and classify objects by evaluating whether they were likely to have been generated by those mechanisms. In 3 experiments, participants were taught causal knowledge that related the features of a novel category. Causal-model theory provided a good quantitative account of the effect of this knowledge on the importance of both individual features and interfeature correlations to classification. By enabling precise model fits and interpretable parameter estimates, causal-model theory helps place the theory-based approach to conceptual representation on equal footing with the well-known similarity-based approaches. For the last several decades, research on the topic of categorization has focused on the problem of learning new categories via examples of category members, that is, from empirical observations. The result has been a host of categorization models that are based on representational ideas such as central prototypes, stored exemplars, and variabilized rules, and on processing principles such as similarity, that have considerable explanatory power and experimental support. More recently, the influence of the prior “theoretical ” knowledge that learners often contribute to their representations of categories has also been a topic of study (Carey,

this article. We also thank Denise Hatton, Tisha Baldwin, Joshua Nathan, Helen Sullivan, and Julia Wenzlaff for collecting data. Some of the stimulus materials used in Experiments 1 and 2 are adapted from the stimulus materials used in Rehder and Hastie (1997) and we thank them for inspiring many of the features and objects used in these studies. This project was supported by a National Science Foundation Grant (NSF-SBR 9515085) and a National Institute of Mental Health Grant (RO1 MH57737) given to Woo-kyoung Ahn, a National Science Foundation Graduate Fellowship to Nancy Kim, and a National Institute of Mental Health Postdoctoral Fellowship (MH10888-01A1) to Mary Lassaline

conducted. Forty years of research has assumed that category learning often involves learning to selectively attend to only those stimulus dimensions useful for classification. We confirmed that participants learned to allocate their attention optimally. We also found that learners tend to fixate all stimulus dimensions early in learning. This result obtained despite evidence that participants were also testing one-dimensional rules during this period. Finally, the restriction of eye movements to only relevant dimensions tended to occur only after errors were largely (or completely) eliminated. We interpret these findings as consistent with multiple-systems theories of learning which maximize information input in order to maximize the number of learning modules involved, and which focus solely on relevant information only after one module has solved the learning problem.

conducted. Over 30 studies have shown that the exemplar-based generalized context model (GCM) usually provides a better quantitative account of 5–4 learning data as compared with the prototype model. However, J. D. Smith and J. P. Minda (2000) argued that the GCM is a psychologically implausible account of 5–4 learning because it implies suboptimal attention weights. To test this claim, the authors recorded undergraduates ’ eye movements while the students learned the 5–4 category structure. Eye fixations matched the attention weights estimated by the GCM but not those of the prototype model. This result confirms that the GCM is a realistic model of the processes involved in learning the 5–4 structure and that learners do not always optimize attention, as commonly supposed. The conditions under which learners are likely to optimize attention during category learning are discussed.

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,

An extension of Nosofsky and Palmeri’s (Psychol. Rev. 104 (1997a) 266) exemplar-based random-walk (EBRW) model of categorization is presented as a model of the time course of categorization of separable-dimension stimuli. Nosofsky and Palmeri (1997a) assumed that the perceptual encoding of all stimuli was identical. However, in the current model, we assume as in Lamberts (J. Exp. Psychol.: General 124 (1995) 161) that the inclusion of individual stimulus dimensions into the similarity calculations is a stochastic process with the probability of inclusion based on the perceptual salience of the dimensions. Thus, the exemplars that enter into the random-walk change dynamically during the time course of processing. This model is implemented as a Markov chain. Its predictions are compared with alternative models in a speeded categorization experiment with separable-dimension stimuli.

We formalize and provide tests of a set of logical-rule models for predicting perceptual classification response times (RTs) and choice probabilities. The models are developed by synthesizing mental-architecture, random-walk, and decision-bound approaches. According to the models, people make independent decisions about the locations of stimuli along a set of component dimensions. Those independent decisions are then combined via logical rules to determine the overall categorization response. The time course of the independent decisions is modeled via random-walk processes operating along individual dimensions. Alternative mental architectures are used as mechanisms for combining the independent decisions to implement the logical rules. We derive fundamental qualitative contrasts for distinguishing among the predictions of the rule models and major alternative models of classification RT. We also use the models to predict detailed RT distribution data associated with individual stimuli in tasks of speeded perceptual classification.

Four observers performed matching, identification, and categorization with stimuli that varied along the integral dimensions: brightness and saturation. General recognition theory (F. G. Ashby & J. T. Townsend, 1986) was applied to quantify the separate influences of perceptual and decisional processes within and across tasks, with a focus on separating perceptual from decisional attention processes. Good accounts of the identification data were obtained from perceptual matching representation. This perceptual representation provided a good account of the categorization data, except when decisional selective attention to 1 stimulus dimension was required. Decisional selective attention reduced the attendeddimension perceptual variance relative to the unattended-dimension perceptual variance, with a larger reduction resulting when brightness, as opposed to saturation was attended. Implications for color vision research are discussed. The ability to predict performance across a variety of related tasks from the physical properties of the stimulus and the nature of the task is a fundamental goal of psychological inquiry. Two tasks that are of particular importance to the survival of all biological organisms are stimulus identification and categorization (Ashby &

Abstract—The abstraction of patterns from data and the formation of categories is a hallmark of human cognitive ability. As such, it has been studied from many different perspectives by researchers, and these studies have led to several explanatory models. In this paper, we consider the inference of categorical representations for the purpose of producing taskspecific responses. Task-relevant responses require a knowledge repertoire that is organized to allow efficient access to useful information. We present a neurodynamical system that infers functionally coherent categories from semantic inputs (or concepts) presented sequentially in different contexts, and encodes them as attractors in a two-dimensional topological feature space. The resulting category representations can then act as pointers in a larger system for semantic cognition. The system allows controlled hierarchical organization and functional segregation of the inferred categories. I.

The effect of a response deadline on categorical decisions was investigated. Time available for response was manipulated in the test phase, along with stimulus difficulty. Effects of these manipulations were observed in response accuracy, and in the mean, standard deviation and skew of the reaction times. The effects observed demonstrate that participants responded to the deadline in an adaptive manner - reducing their reaction time to long-latency decisions whilst leaving short latency decisions relatively unaffected. A simple connectionist model of categorical decisions (Wills & McLaren, 1997) is shown to account for this behavior.