Comparing decision bound and exemplar models of categorization

Maddox, W. Todd; Ashby, F. Gregory

doi:10.3758/bf03211715

Cited by 349 publications

(584 citation statements)

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“…Thus, for the EBRW to roughly match the accuracy predictions yielded by the GCM, modifications in the values of some of the other free parameters would need to be made. It is interesting to note that Maddox and Ashby (1993) found that, when fitting the data of highly experienced individual participants, the GCM does underpredict the observed accuracies, so the EBRW may represent an improvement in this regard. We consider these issues in more depth in the General Discussion.…”

Section: Relations Among Modelsmentioning

confidence: 97%

“…Indeed, Maddox and Ashby (1993) proposed an extended version of the GCM with precisely this type of response rule to allow the model to potentially account for the deterministic responding exhibited by highly experienced individual participants. ~° McKinley and Nosofsky (1995) found that this extended response rule yielded fits to Maddox and Ashby's (1993) data that were far better than those of the GCM and as good as important representatives from the class of decision boundary models. Thus, at least in situations in which models are fitted to individual-participant accuracy data from highly experienced observers, the EBRW appears to provide a major improvement over the GCM.…”

Section: Probabilistic Versus Deterministic Response Rules and Overalmentioning

confidence: 99%

“…A critical question, therefore, is whether or not an EBRW 10 In the response rule proposed by Maddox and Ashby (1993), which did not involve any processing considerations pertaining to classification response time, the K parameter was real-valued, whereas in the EBRW it is integer-valued. In the EBRW, if one allows probability mixtures in the setting of K across trials, the real-valued version of the response rule can be well approximated.…”

Section: Probabilistic Versus Deterministic Response Rules and Overalmentioning

confidence: 99%

See 2 more Smart Citations

An exemplar-based random walk model of speeded classification.

Nosofsky¹,

Palmeri²

1997

Psychological Review

691

984

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The authors propose and test an exemplar-based random walk model for predicting response times in tasks of speeded, multidimensional perceptual classification. The model combines elements of R.M. Nosofsky's (1986) generalized context model of categorization and G. D. Logan's (1988) instance-based model of automaticity. In the model, exemplars race among one another to be retrieved from memory, with rates determined by their similarity to test items. The retrieved exemplars provide incremental information that enters into a random walk process for making classification decisions. The model predicts correctly effects of within-and between-categories similarity, individual-object familiarity, and extended practice on classification response times. It also builds bridges between the domains of categorization and automaticity.Models of multidimensional perceptual classification have grown increasingly powerful and sophisticated in recent years, providing detailed quantitative accounts of patterns of classification learning, transfer, and generalization (e.g., Anderson, 1991;Ashby, 1992;Estes, 1986Estes, , 1994Nosofsky, 1992b;Shanks & Gluck, 1994). However, a fundamental limitation of all the major competing models in the field today is that they offer no processing account of the time course of classification. Because response times provide a window into understanding the nature of cognitive representations and decision processes, it is vital to move in the direction of models that account for this form of data. In this article we propose and test a process-oriented model for predicting response times in tasks of speeded perceptual classification.Our proposed model follows in the spirit of some leading extant models of categorization by assuming that people represent categories in terms of stored exemplars (Hintzman, 1986;Medin & Schaffer, 1978;Nosofsky, 1986). Classification decisions are made by retrieving these stored exemplars from memory. In the newly proposed model, retrieved exemplars are used to drive a random walk process (e.g., Luce, 1986;Townsend & Ashby, 1983) in which evidence accrues to alternative categories over time. Random-walk models have been successful at accounting for performance in tasks of memory, decision making, sensory discrimination, and unidimensional absolute judgment (e.g., Busemeyer, 1985; Karpiuk, Lacouture, & Marley, in press; This work was supported by Grant PHS RO1 MH48494-05 from the National Institute of Mental Health.Jerome Busemeyer, A. A. J. Marley, Richard Shiffrin, and James Townsend provided extensive commentary and discussion. We also wish to thank John Anderson, Rob Goldstone, John Kruschke, Roger Ratcliff, Roger Shepard, and Trisha van Zandt for their comments, discussion, and advice.Correspondence concerning this article should be addressed to Robert M. Nosofsky, Department of Psychology, Indiana University, Bloomington, Indiana 47405; or to Thomas J. Palmeri, Department of Psychology, Vanderbilt University, Nashville, Tennessee 37240. Electronic mail may be sent vi...

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Section: Relations Among Modelsmentioning

confidence: 97%

Section: Probabilistic Versus Deterministic Response Rules and Overalmentioning

confidence: 99%

Section: Probabilistic Versus Deterministic Response Rules and Overalmentioning

confidence: 99%

See 1 more Smart Citation

An exemplar-based random walk model of speeded classification.

Nosofsky¹,

Palmeri²

1997

Psychological Review

691

984

View full text Add to dashboard Cite

show abstract

“…This result is not inconsistent with the hypothesis that the two different types of training result in different types of category representation, but it also suggests that the type of transfer trial influenced which representation participants appeared to be using. Using a psychologically plausible generalization of the original GCM (Maddox & Ashby, 1993;Nosofsky, Gluck, Palmeri, McKinley, & Glauthier, 1994) that has now become a standard part of the model, Kruschke, Johansen, and Blair (1999) showed that this standard exemplar model (detailed below) can reasonably account for all of Yamauchi and Markman's (1998) six conditions, as shown in Table 3. The added parameter indexes response confidence and specifies the degree to which a given difference in evidence for one response over another should translate into a difference in response probabilities.…”

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confidence: 99%

Category Representation for Classification and Feature Inference.

Johansen

Kruschke

2005

Journal of Experimental Psychology: Learning, Memory, and Cogni

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This research's purpose was to contrast the representations resulting from learning of the same categories by either classifying instances or inferring instance features. Prior inference learning research, particularly T. Yamauchi and A. B. Markman (1998), has suggested that feature inference learning fosters prototype representation, whereas classification learning encourages exemplar representation. Experiment 1 supported this hypothesis. Averaged and individual participant data from transfer after inference training were better fit by a prototype than by an exemplar model. However, Experiment 2, with contrasting inference learning conditions, indicated that the prototype model was mimicking a set of label-based bidirectional rules, as determined by the inference learning task demands in Experiment 1. Only the set of rules model accounted for all the inference learning conditions in these experiments.

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“…We review two different multivariate designs (shown in Figures 1C and 1E). Maddox and Ashby (1993) had participants classify rectangles that varied in two dimensions: height and width. Category membership was determined by assuming that each category was distributed over the height and width of the rectangles as an uncorrelated bivariate normal ( Figure 1C).…”

mentioning

confidence: 99%

Comparing categorization models.

Rouder¹,

Ratcliff²

2004

Journal of Experimental Psychology: General

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Four experiments are presented that competitively test rule-and exemplar-based models of human categorization behavior. Participants classified stimuli that varied on a unidimensional axis into 2 categories. The stimuli did not consistently belong to a category; instead, they were probabilistically assigned. By manipulating these assignment probabilities, it was possible to produce stimuli for which exemplar-and rule-based explanations made qualitatively different predictions.F. G. Ashby and J. T. Townsend's (1986) rule-based general recognition theory provided a better account of the data than R. M. Nosofsky's (1986) exemplar-based generalized context model in conditions in which the to-be-classified stimuli were relatively confusable. However, generalized context model provided a better account when the stimuli were relatively few and distinct. These findings are consistent with multiple process accounts of categorization and demonstrate that stimulus confusion is a determining factor as to which process mediates categorization.In this article we present an empirical paradigm to test different theories of categorization behavior. One theory we test is the exemplar-based theory in which categories are represented by sets of stored exemplars. Category membership of a stimulus is determined by similarity of the stimulus to these exemplars (e.g., Medin & Schaffer, 1978;Nosofsky, 1986Nosofsky, , 1987Nosofsky, , 1991). An exemplar-based process relies on retrieval of specific trace-based information without further abstraction; for example, a person is judged as "tall" if he or she is similar in height to others who are considered "tall." The other theory we test is rule-based or decisionbound theory. Decisions are based on an abstracted rule. The relevant space is segmented into regions by bounds, and each region is assigned to a specific category (e.g., Ashby & Gott, 1988;Ashby & Maddox, 1992Ashby & Perrin, 1988;Ashby & Townsend, 1986;Trabasso & Bower, 1968). For example, a person might be considered tall if he or she is perceived as being over 6 ft (1.83 m). The essence of a rule-based process is that processing is based on greatly simplified abstractions or rules but not on the specific trace-based information itself.Both rule-and exemplar-based theories have gained a large degree of support in the experimental literature. There are both exemplar-based models (e.g., generalized context model; Nosofsky, 1986) and rule-based models (e.g., general recognition theory; Ashby & Gott, 1988;Ashby & Townsend, 1986) that can explain a wide array of behavioral data across several domains. Despite the many attempts to discriminate between these two explanations, there have been few decisive tests. Across several paradigms and domains, rule-and exemplarbased predictions often mimic each other.

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Comparing decision bound and exemplar models of categorization

Cited by 349 publications

References 52 publications

An exemplar-based random walk model of speeded classification.

An exemplar-based random walk model of speeded classification.

Category Representation for Classification and Feature Inference.

Comparing categorization models.

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