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Nosofsky, Robert M.; Little, Daniel R.; Donkin, Christopher; Fific, Mario – Psychological Review, 2011
Exemplar-similarity models such as the exemplar-based random walk (EBRW) model (Nosofsky & Palmeri, 1997b) were designed to provide a formal account of multidimensional classification choice probabilities and response times (RTs). At the same time, a recurring theme has been to use exemplar models to account for old-new item recognition and to…
Descriptors: Short Term Memory, Classification, Probability, Cognitive Development
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Soto, Fabian A.; Wasserman, Edward A. – Psychological Review, 2010
A wealth of empirical evidence has now accumulated concerning animals' categorizing photographs of real-world objects. Although these complex stimuli have the advantage of fostering rapid category learning, they are difficult to manipulate experimentally and to represent in formal models of behavior. We present a solution to the representation…
Descriptors: Animals, Classification, Photography, Visual Stimuli
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Kubovy, Michael; van den Berg, Martin – Psychological Review, 2008
The authors investigated whether the gestalt grouping principles can be quantified and whether the conjoint effects of two grouping principles operating at the same time on the same stimuli differ from the sum of their individual effects. After reviewing earlier attempts to discover how grouping principles interact, they developed a probabilistic…
Descriptors: Proximity, Information Retrieval, Psychotherapy, Models
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Fific, Mario; Little, Daniel R.; Nosofsky, Robert M. – Psychological Review, 2010
We formalize and provide tests of a set of logical-rule models for predicting perceptual classification response times (RTs) and choice probabilities. The models are developed by synthesizing mental-architecture, random-walk, and decision-bound approaches. According to the models, people make independent decisions about the locations of stimuli…
Descriptors: Visual Stimuli, Models, Classification, Probability
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Anderson, John R. – Psychological Review, 1991
A rational model of human categorization behavior is presented that assumes that categorization reflects the derivation of optimal estimates of the probability of unseen features of objects. A case is made that categorization behavior can be predicted from the structure of the environment. (SLD)
Descriptors: Adjustment (to Environment), Bayesian Statistics, Behavior Patterns, Classification