Literature DB >> 32170595

A dimensional summation account of polymorphous category learning.

Andy J Wills1, Lyn Ellett2, Fraser Milton3, Gareth Croft4, Tom Beesley5.   

Abstract

Polymorphous concepts are hard to learn, and this is perhaps surprising because they, like many natural concepts, have an overall similarity structure. However, the dimensional summation hypothesis (Milton and Wills Journal of Experimental Psychology: Learning, Memory and Cognition, 30, 407-415 2004) predicts this difficulty. It also makes a number of other predictions about polymorphous concept formation, which are tested here. In Experiment 4, we confirm the theory's prediction that polymorphous concept formation should be facilitated by deterministic pretraining on the constituent features of the stimulus. This facilitation is relative to an equivalent amount of training on the polymorphous concept itself. In further experiments, we compare the predictions of the dimensional summation hypothesis with a more general strategic account (Experiment 2), a seriality of training account (Experiment 3), a stimulus decomposition account (also Experiment 3), and an error-based account (Experiment 4). The dimensional summation hypothesis provides the best account of these data. In Experiment 5, a further prediction is confirmed-the single feature pretraining effect is eliminated by a concurrent counting task. The current experiments suggest the hypothesis that natural concepts might be acquired by the deliberate serial summation of evidence. This idea has testable implications for classroom learning.

Keywords:  Categorization; Dual-process theory; Family resemblance; Overall similarity

Mesh:

Year:  2020        PMID: 32170595     DOI: 10.3758/s13420-020-00409-6

Source DB:  PubMed          Journal:  Learn Behav        ISSN: 1543-4494            Impact factor:   1.986


  44 in total

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Authors:  H S TERRACE
Journal:  J Exp Anal Behav       Date:  1963-01       Impact factor: 2.468

2.  Neural correlates of rule-based and information-integration visual category learning.

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Journal:  Cereb Cortex       Date:  2006-01-25       Impact factor: 5.357

3.  Formation of category representations.

Authors:  A J Wills; Malia Noury; Nicholas J Moberly; Matthew Newport
Journal:  Mem Cognit       Date:  2006-01

4.  Is overall similarity classification less effortful than single-dimension classification?

Authors:  Andy J Wills; Fraser Milton; Christopher A Longmore; Sarah Hester; Jo Robinson
Journal:  Q J Exp Psychol (Hove)       Date:  2012-08-17       Impact factor: 2.143

5.  Processes of overall similarity sorting in free classification.

Authors:  Fraser Milton; Christopher A Longmore; A J Wills
Journal:  J Exp Psychol Hum Percept Perform       Date:  2008-06       Impact factor: 3.332

6.  Dissociable learning processes, associative theory, and testimonial reviews: A comment on Smith and Church (2018).

Authors:  Andy J Wills; Charlotte E R Edmunds; Mike E Le Pelley; Fraser Milton; Ben R Newell; Dominic M Dwyer; David R Shanks
Journal:  Psychon Bull Rev       Date:  2019-12

Review 7.  Bayesian Versus Orthodox Statistics: Which Side Are You On?

Authors:  Zoltan Dienes
Journal:  Perspect Psychol Sci       Date:  2011-05

8.  A Comparison of the neural correlates that underlie rule-based and information-integration category learning.

Authors:  Kathryn L Carpenter; Andy J Wills; Abdelmalek Benattayallah; Fraser Milton
Journal:  Hum Brain Mapp       Date:  2016-05-20       Impact factor: 5.038

9.  Brain activity across the development of automatic categorization: a comparison of categorization tasks using multi-voxel pattern analysis.

Authors:  Fabian A Soto; Jennifer G Waldschmidt; Sebastien Helie; F Gregory Ashby
Journal:  Neuroimage       Date:  2013-01-17       Impact factor: 6.556

10.  Human learning of elemental category structures: revising the classic result of Shepard, Hovland, and Jenkins (1961).

Authors:  Kenneth J Kurtz; Kimery R Levering; Roger D Stanton; Joshua Romero; Steven N Morris
Journal:  J Exp Psychol Learn Mem Cogn       Date:  2012-07-16       Impact factor: 3.051

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  1 in total

1.  An adaptive linear filter model of procedural category learning.

Authors:  Nicolás Marchant; Enrique Canessa; Sergio E Chaigneau
Journal:  Cogn Process       Date:  2022-05-05
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