Literature DB >> 10714140

Learning categories composed of varying instances: the effect of classification, inference, and structural alignment.

T Yamauchi1, A B Markman.   

Abstract

The members of a natural category are not usually identical in their appearance, although at some level they can be described as having features in common. For example, birds have wings, but the actual appearance of their wings varies from one bird to another. To examine the effect of this feature variation on category acquisition, subjects in three experiments were asked to learn categories in which individual features were depicted with several different instances. The results of the experiments indicated that subjects had significant difficulty learning these categories when they were given a standard classification learning task. In contrast, subjects were able to acquire the same categories when they were given an inference learning task, in which they learned the categories by predicting a missing feature of a stimulus given the category label and information about the other features. Finally, subjects who were allowed to compare stimuli during learning were able to learn the categories. These results suggest that a common description of different instances emerges in the process of aligning stimuli.

Mesh:

Year:  2000        PMID: 10714140     DOI: 10.3758/bf03211577

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


  10 in total

Review 1.  The development of features in object concepts.

Authors:  P G Schyns; R L Goldstone; J P Thibaut
Journal:  Behav Brain Sci       Date:  1998-02       Impact factor: 12.579

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

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

3.  Structural alignment in similarity and difference judgments.

Authors:  A B Markman
Journal:  Psychon Bull Rev       Date:  1996-06

4.  Structural alignment in induction and similarity.

Authors:  M E Lassaline
Journal:  J Exp Psychol Learn Mem Cogn       Date:  1996-05       Impact factor: 3.051

5.  Attention, similarity, and the identification-categorization relationship.

Authors:  R M Nosofsky
Journal:  J Exp Psychol Gen       Date:  1986-03

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.  On the genesis of abstract ideas.

Authors:  M I Posner; S W Keele
Journal:  J Exp Psychol       Date:  1968-07

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.  Predictions from uncertain categorizations.

Authors:  G L Murphy; B H Ross
Journal:  Cogn Psychol       Date:  1994-10       Impact factor: 3.468

10.  The role of theories in conceptual coherence.

Authors:  G L Murphy; D L Medin
Journal:  Psychol Rev       Date:  1985-07       Impact factor: 8.934

  10 in total
  9 in total

1.  Consistent contrast aids concept learning.

Authors:  D Billman; D Dávila
Journal:  Mem Cognit       Date:  2001-10

2.  The effect of category learning on sensitivity to within-category correlations.

Authors:  Seth Chin-Parker; Brian H Ross
Journal:  Mem Cognit       Date:  2002-04

3.  Learning abstract relations from using categories.

Authors:  Brian H Ross; Justin L Warren
Journal:  Mem Cognit       Date:  2002-07

4.  Learning categories by making predictions: an investigation of indirect category learning.

Authors:  John Paul Minda; Brian H Ross
Journal:  Mem Cognit       Date:  2004-12

5.  Missing information in multiple-cue probability learning.

Authors:  Chris M White; Derek J Koehler
Journal:  Mem Cognit       Date:  2004-09

6.  Age effects on category learning, categorical perception, and generalization.

Authors:  Caitlin R Bowman; Stefania R Ashby; Dagmar Zeithamova
Journal:  Memory       Date:  2021-11-11

7.  Alignment effects on learning multiple, use-relevant classification systems.

Authors:  Cynthia M Sifonis; Brian H Ross
Journal:  Mem Cognit       Date:  2002-10

8.  Comparison and mapping facilitate relation discovery and predication.

Authors:  Leonidas A A Doumas; John E Hummel
Journal:  PLoS One       Date:  2013-06-25       Impact factor: 3.240

9.  Varying variation: the effects of within- versus across-feature differences on relational category learning.

Authors:  Katherine A Livins; Michael J Spivey; Leonidas A A Doumas
Journal:  Front Psychol       Date:  2015-02-09
  9 in total

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