Literature DB >> 17263065

Ad hoc category restructuring.

Daniel R Little1, Stephan Lewandowsky, Evan Heit.   

Abstract

Participants learned to classify seemingly arbitrary words into categories that also corresponded to ad hoc categories (see, e.g., Barsalou, 1983). By adapting experimental mechanisms previously used to study knowledge restructuring in perceptual categorization, we provide a novel account of how experimental and preexperimental knowledge interact. Participants were told of the existence of the ad hoc categories either at the beginning or the end of training. When the ad hoc labels were revealed at the end of training, participants switched from categorization based on experimental learning to categorization based on preexperimental knowledge in some, but not all, circumstances. Important mediators of the extent of that switch were the amount of performance error experienced during prior learning and whether or not prior knowledge was in conflict with experimental learning. We present a computational model of the trade-off between preexperimental knowledge and experimental learning that accounts for the main results.

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Year:  2006        PMID: 17263065     DOI: 10.3758/bf03195905

Source DB:  PubMed          Journal:  Mem Cognit        ISSN: 0090-502X


  17 in total

1.  Competing strategies in categorization: expediency and resistance to knowledge restructuring.

Authors:  S Lewandowsky; M Kalish; T L Griffiths
Journal:  J Exp Psychol Learn Mem Cogn       Date:  2000-11       Impact factor: 3.051

2.  Category learning with minimal prior knowledge.

Authors:  A S Kaplan; G L Murphy
Journal:  J Exp Psychol Learn Mem Cogn       Date:  2000-07       Impact factor: 3.051

3.  Modeling the effects of prior knowledge on learning incongruent features of category members.

Authors:  Evan Heit; Janet Briggs; Lewis Bott
Journal:  J Exp Psychol Learn Mem Cogn       Date:  2004-09       Impact factor: 3.051

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

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

5.  Error-driven knowledge restructuring in categorization.

Authors:  Michael L Kalish; Stephan Lewandowsky; Melissa Davies
Journal:  J Exp Psychol Learn Mem Cogn       Date:  2005-09       Impact factor: 3.051

6.  Prior knowledge and functionally relevant features in concept learning.

Authors:  E J Wisniewski
Journal:  J Exp Psychol Learn Mem Cogn       Date:  1995-03       Impact factor: 3.051

7.  Rule-plus-exception model of classification learning.

Authors:  R M Nosofsky; T J Palmeri; S C McKinley
Journal:  Psychol Rev       Date:  1994-01       Impact factor: 8.934

8.  The locus of knowledge effects in concept learning.

Authors:  G L Murphy; P D Allopenna
Journal:  J Exp Psychol Learn Mem Cogn       Date:  1994-07       Impact factor: 3.051

9.  Population of linear experts: knowledge partitioning and function learning.

Authors:  Michael L Kalish; Stephan Lewandowsky; John K Kruschke
Journal:  Psychol Rev       Date:  2004-10       Impact factor: 8.934

10.  Rules and exemplars in category learning.

Authors:  M A Erickson; J K Kruschke
Journal:  J Exp Psychol Gen       Date:  1998-06
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  2 in total

1.  Criterion learning in rule-based categorization: simulation of neural mechanism and new data.

Authors:  Sebastien Helie; Shawn W Ell; J Vincent Filoteo; W Todd Maddox
Journal:  Brain Cogn       Date:  2015-02-14       Impact factor: 2.310

2.  Global Cue Inconsistency Diminishes Learning of Cue Validity.

Authors:  Tony S L Wang; Nicole Christie; Piers D L Howe; Daniel R Little
Journal:  Front Psychol       Date:  2016-11-11
  2 in total

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