Literature DB >> 25315925

Individual differences in category learning: memorization versus rule abstraction.

Jeri L Little1, Mark A McDaniel.   

Abstract

Although individual differences in category-learning tasks have been explored, the observed differences have tended to represent different instantiations of general processes (e.g., learners rely upon different cues to develop a rule) and their consequent representations. Additionally, studies have focused largely on participants' categorizations of transfer items to determine the representations that they formed. In the present studies, we used a convergent-measures approach to examine participants' categorizations of transfer items in addition to their self-reported learning orientations and response times on transfer items, and in doing so, we garnered evidence that qualitatively distinct approaches in explicit strategies for category learning (i.e., memorization vs. abstracting an articulable rule) and consequent representations might emerge in a single task. Participants categorized instances that followed a categorization rule (in Study 1, we used a relational rule; in Study 2, an additional task with a single-feature rule). Critically, for both tasks, some transfer items differed from trained instances on only one attribute (but otherwise were perceptually similar), rendering the item a member of the opposing category on the basis of the rule (i.e., termed ambiguous items). Some learners categorized ambiguous items on the basis of perceptual similarity, whereas others categorized them on the basis of an abstracted rule. Self-reported learning orientation (i.e., memorization vs. rule abstraction) predicted categorizations and response times on transfer items. Differences in learning orientations were not associated with performance on other cognitive measures (i.e., working memory capacity and Raven's Advanced Progressive Matrices). This work suggests that individuals may have different predispositions toward memorization versus rule abstraction in a single categorization task.

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Year:  2015        PMID: 25315925     DOI: 10.3758/s13421-014-0475-1

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


  35 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.  Relational similarity and the nonindependence of features in similarity judgments.

Authors:  R L Goldstone; D L Medin; D Gentner
Journal:  Cogn Psychol       Date:  1991-04       Impact factor: 3.468

3.  Working memory capacity and fluid intelligence are strongly related constructs: comment on Ackerman, Beier, and Boyle (2005).

Authors:  Michael J Kane; David Z Hambrick; Andrew R A Conway
Journal:  Psychol Bull       Date:  2005-01       Impact factor: 17.737

4.  Costs and benefits of automatization in category learning of ill-defined rules.

Authors:  Maartje E J Raijmakers; Verena D Schmittmann; Ingmar Visser
Journal:  Cogn Psychol       Date:  2014-01-11       Impact factor: 3.468

5.  Attention and learning processes in the identification and categorization of integral stimuli.

Authors:  R M Nosofsky
Journal:  J Exp Psychol Learn Mem Cogn       Date:  1987-01       Impact factor: 3.051

6.  Varieties of perceptual independence.

Authors:  F G Ashby; J T Townsend
Journal:  Psychol Rev       Date:  1986-04       Impact factor: 8.934

7.  Perceptual manifestations of an analytic structure: the priority of holistic individuation.

Authors:  G Regehr; L R Brooks
Journal:  J Exp Psychol Gen       Date:  1993-03

8.  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

9.  Correlated symptoms and simulated medical classification.

Authors:  D L Medin; M W Altom; S M Edelson; D Freko
Journal:  J Exp Psychol Learn Mem Cogn       Date:  1982-01       Impact factor: 3.051

10.  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

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3.  When high working memory capacity is and is not beneficial for predicting nonlinear processes.

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4.  Rule abstraction, model-based choice, and cognitive reflection.

Authors:  Hilary J Don; Micah B Goldwater; A Ross Otto; Evan J Livesey
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5.  Category learning strategies in younger and older adults: Rule abstraction and memorization.

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7.  Concurrent Dynamics of Category Learning and Metacognitive Judgments.

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Journal:  Front Psychol       Date:  2016-09-27

8.  Individual Difference Factors in the Learning and Transfer of Patterning Discriminations.

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9.  A trait profile of top and middle managers.

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10.  Occipitotemporal representations reflect individual differences in conceptual knowledge.

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