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,

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ositive side, though, the second person might have some advantage over the first person in learning how to shift gears, because the second person would not have to overcome negative transfer from experience with automatic transmissions. As another example, imagine that you are an explorer visiting a remote island, with the purpose of writing a book about the people that you see there. You bring to this island many forms of prior knowledge that will guide you in learning about these new people. For example, based on your experiences in other places, you would expect to see males and females, younger and older people, shy people and arrogant people. You would also have certain hypotheses at a more abstract level, for example, that the clothes that someone wears may be related to the person's age and gender. (Goodman, 1955, referred to such abstract hypotheses as overhypotheses.) In a way, these biases due to previous knowledge might seem to be undesirable. After all, wouldn't be it be be

to all of the category's features. However, people's knowledge of real-world categories often consists of many "rote " features that are not related to their prior knowledge. Five experiments found that even minimal prior knowledge (1 knowledge-relevant feature and 5 rote features per exemplar) can facilitate category learning. Posttests revealed that although the knowledge aided learning, subjects also acquired the rote features that were not related to knowledge, contradicting predictions of an attentional expla-nation of the knowledge effect. The results of Experiment 6 suggested that subjects attempt to link even rote features to their knowledge.

Exemplar, prototype, and connectionist models typically assume that events constitute the basic unit of learning and representation in categorization. In these models, each learning event updates a statistical representation of a category independently of other learning events. An implication is that events involving the same individual affect learning independently and are not integrated into a single structure that represents the individual in an internal model of the world. A series of experiments demonstrates that human subjects track individuals across events, establish representations of them, and use these representations in categorization. These findings are consistent with ‘‘representationalism,’ ’ the view that an internal model of the world constitutes a physical level of representation in the brain, and that the brain does not simply capture the statistical properties of events in an undifferentiated dynamical system. Although categorization is an inherently statistical process that produces generalization, pattern completion, frequency effects, and adaptive learning, it is also an inherently representational process that establishes an internal model of the world. As a result, representational structures evolve in memory to track the histories of individuals, accumulate information about them, and simulate

Introduction In most applications of formal models of categorization, category learning is portrayed as the building-up of a representation in memory for members of the category that have been observed. This assumption is perhaps the most basic that is made for models of categorization, that the representation of a category describes its observed members. Yet if category representations are to serve a purpose such as recognizing new members of a category, then simply relying on memory for known members would be a poor strategy in many situations. For example, if you are learning to distinguish the Smith family from the Jones family, and you have observed a tall, red-haired 45 year old woman who is the mother in the Smith family, and you then see another tall, red-haired 45 year old woman, you would probably classify her as belonging to the Jones family, despite her similarity to an observed member of the Smith family. This example highlights the point that when few members of a categor

In an experiment, subjects learned about new categories for which they had prior beliefs, and made probability judgments at various points during the course of learning. The responses were analyzed in terms of bias due to prior beliefs and in terms of sensitivity to the content of the new categories. These results were compared to the predictions of four models of belief revision or categorization: (1) a Bayesian estimation procedure (Raiffa & Schlaifer, 1961); (2) the integration model (Heit, 1993, 1994), a categorization model that is a generalization of the Bayesian model; (3) a linear operator model that performs serial averaging (Bush & Mosteller, 1955); and (4) a simple adaptive network model of categorization (Gluck & Bower, 1988) that is a generalization of the linear operator model. Subjects were conservative in terms of sensitivity to new information, compared to the predictions of the Bayesian model and the linear operator model. The network model was able to...

R. M. Nosofsky and T. J. Palmeri's (1997) exemplar-based random-walk (EBRW) model of speeded classification is extended to account for speeded same-different judgments among integral-dimension stimuli. According to the model, an important component process of same-different judgments is that people store individual examples of experienced same and different pairs of objects in memory. These exemplar pairs are retrieved from memory on the basis of how similar they are to a currendy presented pair of objects. The retrieved pairs drive a random-walk process for making same-different decisions. The EBRW predicts correctly that same responses are faster for objects lying in isolated than in dense regions of similarity space. The model also predicts correctly effects of same-identity versus samecategory instructions and is sensitive to observers ' past experiences with specific same and different pairs of objects. The main tenet of exemplar-based models of cognition is that people store particular instances of events in memory, called exemplars, and that these exemplars are later retrieved to perform a particular task. Exemplar-based models have long been used to model performance in categorization tasks (Medin & Schaffer, 1978). These models assume that people represent categories as a set of exemplars and make categorization decisions by retrieving exemplars from memory. Such models have rendered accurate

According to the instantiation principle, the representation of a category includes detailed information about its diverse range of instances. Many accounts of categorization, including classical and standard prototype theories, do not follow the instantiation principle, because they assume that detailed, exemplar-level information is filtered out of category representations. Nevertheless, the instantiation principle can be implemented in a wide class of models, including both exemplar and abstraction models. To assess the instantiation principle empirically, a parameter-free exemplarbased model of instantiation was applied to typicality judgments for 16 simple categories (e.g., mammal, beverage) and 14 complex categories (e.g., dangerous mammal) in four superordinates (animal, food, small animal, dangerous animal). Across three studies, the model did an excellent job of predicting mean typicality judgments (correlations generally above .9) and a good job of predicting standard deviati...

Experiments were conducted in which observers learned to classify simple perceptual stimuli into low-variability and high-variability categories. Similarities between objects were measured in independent psychological-scaling tasks. The results showed that observers classified transfer stimuli into the high-variability categories with greater probability than was predicted by a baseline version of an exemplar-similarity model. Qualitative evidence for the role of category variability on perceptual classification, which could not be explained in terms of the baseline exemplar-similarity model, was obtained as well. Possible accounts of the effects of category variability are considered in the General Discussion section. According to exemplar models of perceptual classification, people represent categories by storing individual exemplars in memory and classify objects on the basis of their similarity to these stored exemplars (Hintzman, 1986; Medin & Schaffer, 1978; Nosofsky, 1986). Exemplar models have been successful at predicting a wide variety of perceptual classification phenomena, including details of classification learning, patterns of generalization to new transfer stimuli, and the time course of classification decision making. In the present research, however, we pursued an avenue that may demonstrate a fundamental limitation of these models. The key previous study that motivated the present work was the classic set of experiments reported by Rips (1989) on the role of category variability in classification judgment. An example of one of these experiments is as follows. Participants were asked to imagine a circular object with a 3-in. diameter. One group of participants was asked whether