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A dimensional summation account of polymorphous category learning

A dimensional summation account of polymorphous category learning
A dimensional summation account of polymorphous category learning
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.
1543-4494
66-83
Wills, Andy
ac3dacc2-7918-47e9-9b4f-bbfde29a4ebf
Ellett, Lyn
96482ea6-04b6-4a50-a7ec-ae0a3abc20ca
Milton, Fraser
383d40fb-7a24-4086-b58d-ece5deea0910
Croft, Gareth
ce070fb2-8f69-46c5-beab-b192042970ea
Beesley, Tom
08df285a-0bc2-42f4-9f0e-33f3a66ae6e4
Wills, Andy
ac3dacc2-7918-47e9-9b4f-bbfde29a4ebf
Ellett, Lyn
96482ea6-04b6-4a50-a7ec-ae0a3abc20ca
Milton, Fraser
383d40fb-7a24-4086-b58d-ece5deea0910
Croft, Gareth
ce070fb2-8f69-46c5-beab-b192042970ea
Beesley, Tom
08df285a-0bc2-42f4-9f0e-33f3a66ae6e4

Wills, Andy, Ellett, Lyn, Milton, Fraser, Croft, Gareth and Beesley, Tom (2020) A dimensional summation account of polymorphous category learning. Learning & Behavior, 48, 66-83. (doi:10.3758/s13420-020-00409-6).

Record type: Article

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.

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Wills_et_al._2020_poly_preprint - Accepted Manuscript
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Published date: 13 March 2020

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Local EPrints ID: 453712
URI: http://eprints.soton.ac.uk/id/eprint/453712
ISSN: 1543-4494
PURE UUID: 2947e35a-7a6f-47d1-8343-18462ba7dd6a
ORCID for Lyn Ellett: ORCID iD orcid.org/0000-0002-6051-3604

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Date deposited: 21 Jan 2022 17:34
Last modified: 17 Mar 2024 04:10

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Contributors

Author: Andy Wills
Author: Lyn Ellett ORCID iD
Author: Fraser Milton
Author: Gareth Croft
Author: Tom Beesley

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