this article should be addressed to Robert Goldstone, Psychology Department, Indiana University, Bloomington, Indiana 47405. Electronic mail may be sent to rgoldsto @ ucs.indiana.edu

The question of how one should decide among competing explanations of data is at the heart of the scientific enterprise. Computational models of cognition are increasingly being advanced as explanations of behavior. The success of this line of inquiry depends on the development of robust methods to guide the evaluation and selection of these models. This article introduces a method of selecting among mathematical models of cognition known as minimum description length, which provides an intuitive and theoretically well-grounded understanding of why one model should be chosen. A central but elusive concept in model selection, complexity, can also be derived with the method. The adequacy of the method is demonstrated in 3 areas of cognitive modeling: psychophysics, information integration, and categorization. How should one choose among competing theoretical explanations of data? This question is at the heart of the scientific enterprise, regardless of whether verbal models are being tested in an experimental setting or computational models are being evaluated in simulations. A number of criteria have been proposed to assist in this endeavor, summarized nicely by Jacobs and Grainger

Two connectionist models of attention in associative learning, previously used to model human category learning, are shown to have special cases that are essentially equivalent to N. J. Mackintosh's (1975, Psychological Review, 82, 276 298) classic model of attention in animal learning. The models unify formulas for associative weight change with formulas for attentional change, under a common goal of error reduction. Error-driven attentional shifting accelerates learning of new associations but also protects previously learned associations from retroactive interference. The models are fit to data from a recent experiment in human associative learning (J. K. Kruschke 6 N. J. Blair, 2000, Psychonomic Bulletin 6 Review, 7, 636 645), which shows that blocking of learning involves learned inattention. The approach also provides a novel and unifying theory of latent inhibition (the preexposure effect) in terms of blocking. The discussion summarizes how the approach accounts for a variety of other ``irrational' ' phenomena in associative learning, including base rate effects, perseveration of attention through relevance

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,

predictions of exemplar models and that supported prototype models. In the authors ’ view, this evidence confounded the issue of the nature of the category representation with the type of response rule (probabilistic vs. deterministic) that was used. Also, their designs did not test whether the prototype models correctly predicted generalization performance. The present work demonstrates that an exemplar model that includes a response-scaling mechanism provides a natural account of all of Smith et al.’s experimental results. Furthermore, the exemplar model predicts classification performance better than the prototype models when novel transfer stimuli are included in the experimental designs. A classic issue in cognitive psychology concerns the manner in which people represent categories in memory. According to prototype models (Homa, 1984; Posner & Keele, 1968; Reed, 1972), people represent categories by forming a summary representation that is a central tendency of all of the experienced members of a

Introduction A timbre recognition experiment to classify 39 different orchestral instrument timbres was conducted using an exemplar-based learning system. The data consisted of the steady-state spectrum of each of the instruments played at different pitches (Sandell 1994). It has been shown that the attack portion of a musical instrument is important for identification tasks. Yet other studies show that steady-state portion is also significant (Grey 1978; Kendall and Carterette 1986). In addition to the spectral data, the moments of the spectrum, including the centroid, were considered as potential features for the identification process. The implementation of the identification task is based on a combination of a k-nearest neighbor (k-NN) classifier and a genetic algorithm, which is used for feature selection and feature weighting. This paradigm, also known as the exemplar-based learning model (Aha 1997), is attractive because training is not necessary, learning is extremely

A model of memory retrieval is described. The model embodies 4 main claims: (a) temporal memory— traces of items are represented in memory partly in terms of their temporal distance from the present; (b) scale-similarity—similar mechanisms govern retrieval from memory over many different timescales; (c) local distinctiveness—performance on a range of memory tasks is determined by interference from near psychological neighbors; and (d) interference-based forgetting—all memory loss is due to interference and not trace decay. The model is applied to data on free recall and serial recall. The account emphasizes qualitative similarity in the retrieval principles involved in memory performance at all timescales, contrary to models that emphasize distinctions between short-term and long-term memory.

rather than a normal, distribution of scores. Experiment 2 showed that rule-based learning can be disrupted by a dual working memory task even when both dimensions are relevant for optimal categorization. The results support the notion of at least two systems of category learning: a hypothesis-testing system that seeks verbalizable rules and relies on working memory and selective attention, and an implicit system that is procedural-learning based and is essentially automatic. 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

Four observers completed perceptual matching, identification, and categorization tasks using separable-dimension stimuli. A unified quantitative approach relating perceptual matching, identification, and categorization was proposed and tested. The approach derives from general recognition theory (Ashby & Townsend, 1986) and provides a powerful method for quantifying the separate influences of perceptual processes and decisional processes within and across tasks. Good accounts of the identification data were obtained from an initial perceptual representation derived from perceptual matching. The same perceptual representation provided a good account of the categorization data, except when selective attention to one stimulus dimension was required. Selective attention altered the perceptual representation by decreasing the perceptual variance along the attended dimension. These findings suggest that a complete understanding of identification and categorization performance requires an understanding of perceptual and decisional processes. Implications for other psychological tasks are discussed. An important goal of psychological inquiry is to understand how behavior is influenced by the environmental stimulation and the task at hand. Information about the environment

Mental localization efforts tend to stress the where more than the what. We argue that the proper targets for localization are well-specified cognitive models. We make this case by relating an existing cognitive model of category learning to a learning circuit involving the hippocampus, perirhinal, and prefrontal cortices. Results from groups varying in function along this circuit (e.g., infants, amnesics, and older adults) are successfully simulated by reducing the model’s ability to form new clusters in response to surprising events, such as an error in supervised learning or an unfamiliar stimulus in unsupervised learning. Clusters in the model are akin to conjunctive codes that are rooted in an episodic experience (the surprising event) yet can develop to resemble abstract codes as they are updated by subsequent experiences. Thus, the model holds that the line separating episodic and semantic information can become blurred. Dissociations (categorization vs. recognition) are explained in terms of cluster recruitment demands. Franz remarked in his 1912 essay “New Phrenology” that “the individual parts of the brain do not work independently; they work interdependently, and it is because of the possible functional and anatomical connections that certain types or kinds of mental states are more in evidence