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How and why does category learning cause categorical perception?

How and why does category learning cause categorical perception?
How and why does category learning cause categorical perception?
Learning to categorize requires distinguishing category members from non-members by detecting the features that covary with membership. Human subjects were trained to sort visual textures into two categories by trial and error with corrective feedback. Difficulty levels were increased by decreasing the proportion of covariant features. Pairwise similarity judgments were tested before and after category learning. Three effects were observed: (1) The lower the proportion of covariant features, the more trials it took to learn the category and the fewer the subjects who succeeded in learning it. After training, (2) perceived pairwise similarity decreased between categories and, to a lesser extent, (3) increased within categories, at all levels of difficulty, but only for successful learners. This perceived between-category separation and within-category compression is called categorical perception (CP). A very simple neural network model for category learning using uniform binary (0/1) features showed similar CP effects. We hypothesize that CP occurs because learning to selectively detect covariant features and ignore non-covariant features reduces the dimensionality of perceived similarity space. In addition to (1) – (3), the nets showed (4) a strong negative correlation between the proportion of covariant features and the size of the CP effect. This correlation was not evident in the human subjects, probably because, unlike the formal binary features of the input to the nets, which were all uniform, the visual features of the human inputs varied in difficulty.
categorical perception, category learning, neural network modelling, dimensionality reduction
0889-3667
Perez-Gay, Fernanda
f0c9d458-73f6-49e7-8300-2368a85f0220
Christian, Thèriault
e5dfac75-c0f0-460b-b4ea-7819e151ca7f
Gregory, Madeline
0bc3e42c-6ba2-4902-b2de-f78f663f5b3a
Sabri, Hisham
a6f0ae9b-ffed-41d0-af1b-887f8e484125
Harnad, Stevan
442ee520-71a1-4283-8e01-106693487d8b
Rivas, Daniel
be9748c1-e5a6-4986-8fbe-ffa10e110b09
Perez-Gay, Fernanda
f0c9d458-73f6-49e7-8300-2368a85f0220
Christian, Thèriault
e5dfac75-c0f0-460b-b4ea-7819e151ca7f
Gregory, Madeline
0bc3e42c-6ba2-4902-b2de-f78f663f5b3a
Sabri, Hisham
a6f0ae9b-ffed-41d0-af1b-887f8e484125
Harnad, Stevan
442ee520-71a1-4283-8e01-106693487d8b
Rivas, Daniel
be9748c1-e5a6-4986-8fbe-ffa10e110b09

Perez-Gay, Fernanda, Christian, Thèriault, Gregory, Madeline, Sabri, Hisham, Harnad, Stevan and Rivas, Daniel (2017) How and why does category learning cause categorical perception? International Journal of Comparative Psychology, 30.

Record type: Article

Abstract

Learning to categorize requires distinguishing category members from non-members by detecting the features that covary with membership. Human subjects were trained to sort visual textures into two categories by trial and error with corrective feedback. Difficulty levels were increased by decreasing the proportion of covariant features. Pairwise similarity judgments were tested before and after category learning. Three effects were observed: (1) The lower the proportion of covariant features, the more trials it took to learn the category and the fewer the subjects who succeeded in learning it. After training, (2) perceived pairwise similarity decreased between categories and, to a lesser extent, (3) increased within categories, at all levels of difficulty, but only for successful learners. This perceived between-category separation and within-category compression is called categorical perception (CP). A very simple neural network model for category learning using uniform binary (0/1) features showed similar CP effects. We hypothesize that CP occurs because learning to selectively detect covariant features and ignore non-covariant features reduces the dimensionality of perceived similarity space. In addition to (1) – (3), the nets showed (4) a strong negative correlation between the proportion of covariant features and the size of the CP effect. This correlation was not evident in the human subjects, probably because, unlike the formal binary features of the input to the nets, which were all uniform, the visual features of the human inputs varied in difficulty.

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More information

Accepted/In Press date: 14 May 2017
Published date: 2017
Keywords: categorical perception, category learning, neural network modelling, dimensionality reduction

Identifiers

Local EPrints ID: 413622
URI: https://eprints.soton.ac.uk/id/eprint/413622
ISSN: 0889-3667
PURE UUID: 870e5ce0-c01b-4498-8c76-ed2bb7c835ac
ORCID for Stevan Harnad: ORCID iD orcid.org/0000-0001-6153-1129

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Date deposited: 30 Aug 2017 16:31
Last modified: 06 Oct 2018 00:39

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