Literature DB >> 11911384

Comparing prototype-based and exemplar-based accounts of category learning and attentional allocation.

John Paul Minda1, J David Smith.   

Abstract

Exemplar theory was motivated by research that often used D. L. Medin and M. M. Schaffer's (1978) 5/4 stimulus set. The exemplar model has seemed to fit categorization data from this stimulus set better than a prototype model can. Moreover, the exemplar model alone predicts a qualitative aspect of performance that participants sometimes show. In 2 experiments, the authors reexamined these findings. In both experiments, a prototype model fit participants' performance profiles better than an exemplar model did when comparable prototype and exemplar models were used. Moreover, even when participants showed the qualitative aspect of performance, the exemplar model explained it by making implausible assumptions about human attention and effort in categorization tasks. An independent assay of participants' attentional strategies suggested that the description the exemplar model offers in such cases is incorrect. A review of 30 uses of the 5/4 stimulus set in the literature reinforces this suggestion.

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Year:  2002        PMID: 11911384     DOI: 10.1037//0278-7393.28.2.275

Source DB:  PubMed          Journal:  J Exp Psychol Learn Mem Cogn        ISSN: 0278-7393            Impact factor:   3.051


  20 in total

1.  As easy to memorize as they are to classify: the 5-4 categories and the category advantage.

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Journal:  Mem Cognit       Date:  2003-12

2.  Visual noise reveals category representations.

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3.  When parameters collide: a warning about categorization models.

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4.  The divergent autoencoder (DIVA) model of category learning.

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6.  Model evaluation using grouped or individual data.

Authors:  Andrew L Cohen; Adam N Sanborn; Richard M Shiffrin
Journal:  Psychon Bull Rev       Date:  2008-08

7.  Similarity relations in visual search predict rapid visual categorization.

Authors:  Krithika Mohan; S P Arun
Journal:  J Vis       Date:  2012-10-23       Impact factor: 2.240

8.  Training set coherence and set size effects on concept generalization and recognition.

Authors:  Caitlin R Bowman; Dagmar Zeithamova
Journal:  J Exp Psychol Learn Mem Cogn       Date:  2020-02-27       Impact factor: 3.051

9.  Decoding the brain's algorithm for categorization from its neural implementation.

Authors:  Michael L Mack; Alison R Preston; Bradley C Love
Journal:  Curr Biol       Date:  2013-10-03       Impact factor: 10.834

10.  Evidence of metacognitive control by humans and monkeys in a perceptual categorization task.

Authors:  Joshua S Redford
Journal:  J Exp Psychol Learn Mem Cogn       Date:  2010-01       Impact factor: 3.051

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