Literature DB >> 12198775

On the nature of implicit categorization.

F G Ashby1, E M Waldron.   

Abstract

Current categorization models disagree about whether people make a priori assumptions about the structure of unfamiliar categories. Data from two experiments provided strong evidence that people do not make such assumptions. These results rule out prototype models and many decision bound models of categorization. We review previously published neuropsychological results that favor the assumption that category learning relies on a procedural-memory-based system, rather than on an instance-based system (as is assumed by exemplar models). On the basis of these results, a new category-learning model is proposed that makes no a priori assumptions about category structure and that relies on procedural learning and memory.

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Year:  1999        PMID: 12198775     DOI: 10.3758/bf03210826

Source DB:  PubMed          Journal:  Psychon Bull Rev        ISSN: 1069-9384


  45 in total

1.  ALCOVE: an exemplar-based connectionist model of category learning.

Authors:  J K Kruschke
Journal:  Psychol Rev       Date:  1992-01       Impact factor: 8.934

2.  Organization of visual cortical inputs to the striatum and subsequent outputs to the pallido-nigral complex in the monkey.

Authors:  J A Saint-Cyr; L G Ungerleider; R Desimone
Journal:  J Comp Neurol       Date:  1990-08-08       Impact factor: 3.215

Review 3.  Alternative strategies of categorization.

Authors:  E E Smith; A L Patalano; J Jonides
Journal:  Cognition       Date:  1998-01

4.  On the development of procedural knowledge.

Authors:  D B Willingham; M J Nissen; P Bullemer
Journal:  J Exp Psychol Learn Mem Cogn       Date:  1989-11       Impact factor: 3.051

5.  Array models for category learning.

Authors:  W K Estes
Journal:  Cogn Psychol       Date:  1986-10       Impact factor: 3.468

6.  Family resemblance, conceptual cohesiveness, and category construction.

Authors:  D L Medin; W D Wattenmaker; S E Hampson
Journal:  Cogn Psychol       Date:  1987-04       Impact factor: 3.468

7.  Learning about categories in the absence of memory.

Authors:  L R Squire; B J Knowlton
Journal:  Proc Natl Acad Sci U S A       Date:  1995-12-19       Impact factor: 11.205

8.  A neuropsychological theory of multiple systems in category learning.

Authors:  F G Ashby; L A Alfonso-Reese; A U Turken; E M Waldron
Journal:  Psychol Rev       Date:  1998-07       Impact factor: 8.934

9.  The learning of categories: parallel brain systems for item memory and category knowledge.

Authors:  B J Knowlton; L R Squire
Journal:  Science       Date:  1993-12-10       Impact factor: 47.728

10.  Cognitive neuroscience analyses of memory: a historical perspective.

Authors:  M R Polster; L Nadel; D L Schacter
Journal:  J Cogn Neurosci       Date:  1991       Impact factor: 3.225

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  64 in total

1.  The effects of concurrent task interference on category learning: evidence for multiple category learning systems.

Authors:  E M Waldron; F G Ashby
Journal:  Psychon Bull Rev       Date:  2001-03

2.  Simulating the effects of dopamine imbalance on cognition: from positive affect to Parkinson's disease.

Authors:  Sébastien Hélie; Erick J Paul; F Gregory Ashby
Journal:  Neural Netw       Date:  2012-02-20

3.  Procedural learning in perceptual categorization.

Authors:  F Gregory Ashby; Shawn W Ell; Elliott M Waldron
Journal:  Mem Cognit       Date:  2003-10

4.  Observational versus feedback training in rule-based and information-integration category learning.

Authors:  F Gregory Ashby; W Todd Maddox; Corey J Bohil
Journal:  Mem Cognit       Date:  2002-07

Review 5.  A knowledge-resonance (KRES) model of category learning.

Authors:  Bob Rehder; Gregory L Murphy
Journal:  Psychon Bull Rev       Date:  2003-12

6.  Disrupting feedback processing interferes with rule-based but not information-integration category learning.

Authors:  W Todd Maddox; F Gregory Ashby; A David Ing; Alan D Pickering
Journal:  Mem Cognit       Date:  2004-06

7.  Analogical transfer in perceptual categorization.

Authors:  Michael B Casale; Jessica L Roeder; F Gregory Ashby
Journal:  Mem Cognit       Date:  2012-04

8.  A neurocomputational account of cognitive deficits in Parkinson's disease.

Authors:  Sébastien Hélie; Erick J Paul; F Gregory Ashby
Journal:  Neuropsychologia       Date:  2012-06-08       Impact factor: 3.139

9.  Computational Models Inform Clinical Science and Assessment: An Application to Category Learning in Striatal-Damaged Patients.

Authors:  W Todd Maddox; J Vincent Filoteo; Dagmar Zeithamova
Journal:  J Math Psychol       Date:  2010-02-01       Impact factor: 2.223

10.  Initial training with difficult items facilitates information integration, but not rule-based category learning.

Authors:  Brian J Spiering; F Gregory Ashby
Journal:  Psychol Sci       Date:  2008-11
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