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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In this article we present a connectionist model of category learning that takes into account the prior knowledge that people bring to many new learning situations. This model, which we call the Knowledge-Resonance Model or KRES, employs a recurrent network with bidirectional connections which are updated according to a contrastive-Hebbian learning rule. When prior knowledge is incorporated into a KRES network, the KRES activation dynamics and learning procedure accounts for a range of empirical results regarding the effects prior knowledge on category learning, including the accelerated learning that occurs in the presence of knowledge, the reinterpretation of features in light error correcting feedback, and the unlearning of prior knowledge which is inappropriate for a particular category.

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

Many theories of category learning assume that learning is driven by a need to minimize classification error. When there is no classification error, therefore, learning of individual features should be negligible. The authors tested this hypothesis by conducting three category-learning experiments adapted from an associative learning blocking paradigm. Contrary to an error-driven account of learning, participants learned a wide range of information when they learned about categories, and blocking effects were difficult to obtain. Conversely, when participants learned to predict an outcome in a task with the same formal structure and materials, blocking effects were robust and followed the predictions of error-driven learning. The authors discuss their findings in relation to models of category learning and the usefulness of category knowledge in the environment.

An ongoing goal in the field of categorization has been to determine how objects ’ features provide evidence of membership in one category versus another. Well-known findings include that feature diagnosticity is a function of how often the feature appears in category members versus nonmembers, their perceptual salience, how features are used in support of inferences, and how observable features are related to other observable features. We tested how diagnosticity is affected by causal relations between observable and unobserved features. Consistent with our view of classification as diagnostic reasoning, we found that observable features are more diagnostic to the extent that they are caused by underlying features that define category membership, because the presence of the latter can be (causally) inferred from the former. Implications of these results for current views of conceptual structure and models of categorization are discussed. It is generally accepted that people’s concepts include not only the features and attributes of the entity being represented, but also the ways in which those features are related to one another. For example, we know that hormones can alter a person’s behavior, that chemical structure can affect a substance’s hardness, and that processor

Two experiments addressed the novel issue of how people incorporate verbal arguments into category learning. In Experiment 1, at the start of learning subjects were given verbal arguments, which had an influence equivalent to a fixed number of category members. In Experiment 2, subjects learned under slower-paced conditions, and it was found that both prior knowledge and arguments had multiple effects on categorization: a fixed initial influence plus selective weighting of new observations. The results supported the idea that verbally presented arguments can be treated in a similar manner as other forms of prior knowledge, from the perspective of applying models of categorization. Verbal Arguments 3 Recent results have indicated that categorization is influenced not only by the observed members of a category but also by prior knowledge (e.g., Hayes, Taplin, & Munro, 1996; Kaplan & Murphy, 2000; Palmeri & Blalock, 2000; Spalding & Murphy, 1996; Wattenmaker, 1995; Wisniewski & Medin, 1...

This paper is intended to extend the range of categorization research even further by looking at how verbal arguments are incorporated into category learning. For example, a job applicant being recruited by an ambitious company might make observations of the workplace and the people there, but these observations could be accompanied by verbal arguments about why it is a good place to work and why it is better than other places. These arguments could present information (such as regarding employment benefits at this company) that would not be available from direct observations. In some situations, arguments could be used to contradict people's prior knowledge. For example, trainee counselors could be taught that, contrary to popular belief, shy people often attend parties as an attempt to mask their shyness

This article reports the results of an experiment addressing extrapolation in function learning, in particular the issue of whether participants can extrapolate in a nonmonotonic manner. Existing models of function learning, including the extrapolation association model of function learning (EXAM; E. L

this article should be addressed to Evan Heit, Department of Psychology, University of Warwick, Coventry CV4 7AL, United Kingdom. E-mail: e.heit@warwick.ac.uk Journal of Experimental Psychology: Copyright 2004 by the American Psychological Association Learning, Memory, and Cognition 2004, Vol. 30, No. 5, 1065--1081 0278-7393/04/$12.00 DOI: 10.1037/0278-7393.30.5.1065 1065 The main result was that critical features were learned better than filler features, but this advantage of critical features depended on the training block. In the first training block, when participants had little data to use to select from sources of prior knowledge, there was no advantage for critical features. However, with more training blocks, there was an increased advantage for critical features. In other words, at the start of the experiment, people had far too much prior knowledge about buildings for it to be of any use. However, with more observations, it became clear that prior knowledge about churches and office buildings would be particularly useful