The cognitive side of probability learning.

Estes, W. K.

doi:10.1037/0033-295x.83.1.37

Cited by 307 publications

(224 citation statements)

References 48 publications

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“…By probability matching, we mean that if an exemplar receives Category A feedback, for instance, with probability .70, then the observer tends to classify that exemplar into Category A with probability .70. Although probability matching is often observed in probabilistic classification designs (Estes, 1976), research conducted by Ashby and his colleagues indicates that highly experienced individual observers often respond more deterministically (i.e., with probabilities closer to zero or unity) than predicted by a probability matching rule (Ashby & Gott, 1988;Ashby & Maddox, 1992). The EBRW predicts probability matching behavior in the special case in which the response criteria are set at magnitude one.…”

Section: Probabilistic Versus Deterministic Response Rules and Overalmentioning

confidence: 99%

An exemplar-based random walk model of speeded classification.

Nosofsky¹,

Palmeri²

1997

Psychological Review

690

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: Probabilistic Versus Deterministic Response Rules and Overalmentioning

confidence: 99%

An exemplar-based random walk model of speeded classification.

Nosofsky¹,

Palmeri²

1997

Psychological Review

690

984

View full text Add to dashboard Cite

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“…For example, a observer may learn about the strength of different sports teams by viewing the outcomes of their head-to-head competitions (Heit, Price, & Bower, 1994). Likewise, in presidential campaigns, voters gain knowledge of the strength of alternative candidates from the results of the primary elections (Estes, 1976).…”

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confidence: 99%

Limitations of exemplar models of multi-attribute probabilistic inference.

Nosofsky¹,

Bergert²

2007

Journal of Experimental Psychology: Learning, Memory, and Cogni

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Observers were presented with pairs of objects varying along binary-valued attributes and learned to predict which member of each pair had a greater value on a continuously varying criterion variable. The predictions from exemplar models of categorization were contrasted with classic alternative models, including generalized versions of a "take-the-best" model and a weighted-additive model, by testing structures in which interactions between attributes predicted the magnitude of the criterion variable. Under typical training conditions, observers showed little sensitivity to the attribute interactions, thereby challenging the predictions from the exemplar models. In a condition involving highly extended training, observers eventually learned the relations between the attribute interactions and the criterion variable. However, an analysis of the observers' response times for making their paired-comparison decisions also challenged the exemplar model predictions. Instead, it appeared that most observers recoded the interacting attributes into emergent configural cues. They then applied a set of hierarchically organized rules based on the priority of the cues to make their decisions.

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“…Often the degree of variability of a variable is strongly distorted by contextual effects, thus affecting resulting judgments. For example, people tend to base their estimates of frequencies and probabilities on absolute vs relative frequencies (Estes, 1976) Within the knowledge engineering domain it is possible to identify and outline those judgment processes which are not normally completed effectively by the expert, knowledge engineer and the user. Statistical models have been developed or adapted but other types are possible.…”

Section: Judgment and Choice In Knowledge Engineeringmentioning

confidence: 99%