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A Picture is Worth 7.17 Words : Learning Categories from Examples and Definitions. / Moskvichev, Arseny ; Tikhonov, Roman ; Steyvers, Mark.

41st Annual Meeting of the Cognitive Science Society (CogSci 2019): Proceedings. Curran Associates, Inc. , 2019. p. 2406-2412.

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Harvard

Moskvichev, A, Tikhonov, R & Steyvers, M 2019, A Picture is Worth 7.17 Words: Learning Categories from Examples and Definitions. in 41st Annual Meeting of the Cognitive Science Society (CogSci 2019): Proceedings. Curran Associates, Inc. , pp. 2406-2412, 41st Annual Meeting of the Cognitive Science Society, Montréal, Canada, 24/07/19.

APA

Moskvichev, A., Tikhonov, R., & Steyvers, M. (2019). A Picture is Worth 7.17 Words: Learning Categories from Examples and Definitions. In 41st Annual Meeting of the Cognitive Science Society (CogSci 2019): Proceedings (pp. 2406-2412). Curran Associates, Inc.

Vancouver

Moskvichev A, Tikhonov R, Steyvers M. A Picture is Worth 7.17 Words: Learning Categories from Examples and Definitions. In 41st Annual Meeting of the Cognitive Science Society (CogSci 2019): Proceedings. Curran Associates, Inc. . 2019. p. 2406-2412

Author

Moskvichev, Arseny ; Tikhonov, Roman ; Steyvers, Mark. / A Picture is Worth 7.17 Words : Learning Categories from Examples and Definitions. 41st Annual Meeting of the Cognitive Science Society (CogSci 2019): Proceedings. Curran Associates, Inc. , 2019. pp. 2406-2412

BibTeX

@inproceedings{eedbfc254bbf4ea8819b529ba1d1f89a,
title = "A Picture is Worth 7.17 Words: Learning Categories from Examples and Definitions",
abstract = "Both examples and verbal explanations play an important role in learning new concepts and categories. At the same time, learning from verbal explanations is not accounted for in most category learning models, and is not studied in the traditional category learning paradigm. We propose a rational category communication model that formally describes the process of communicating a category structure using both verbal explanations and visual examples in a pedagogical setting. We build our model based on the assumption that verbal instructions are best suited for communication of crude constraints on a category structure, while exemplars complement it by providing means for finer adjustments. Our empirical study demonstrates that verbal communication is indeed more robust to changes in stimuli dimensionality, but that its efficiency is adversely affected when distinguishing between categories requires perceptual precision. Communicating through examples has a reversed pattern. We hope that both the proposed experimental paradigm and the computational model would facilitate further research into the relative roles of verbal and exemplar communication in category learning.",
keywords = "categorization, category learning, computational modelling, communication channels, communication efficiency",
author = "Arseny Moskvichev and Roman Tikhonov and Mark Steyvers",
year = "2019",
language = "English",
isbn = "9781510891555",
pages = "2406--2412",
booktitle = "41st Annual Meeting of the Cognitive Science Society (CogSci 2019)",
publisher = "Curran Associates, Inc. ",
address = "United States",
note = "41st Annual Meeting of the Cognitive Science Society, COGSCI 2019 ; Conference date: 24-07-2019 Through 27-07-2019",

}

RIS

TY - GEN

T1 - A Picture is Worth 7.17 Words

T2 - 41st Annual Meeting of the Cognitive Science Society

AU - Moskvichev, Arseny

AU - Tikhonov, Roman

AU - Steyvers, Mark

PY - 2019

Y1 - 2019

N2 - Both examples and verbal explanations play an important role in learning new concepts and categories. At the same time, learning from verbal explanations is not accounted for in most category learning models, and is not studied in the traditional category learning paradigm. We propose a rational category communication model that formally describes the process of communicating a category structure using both verbal explanations and visual examples in a pedagogical setting. We build our model based on the assumption that verbal instructions are best suited for communication of crude constraints on a category structure, while exemplars complement it by providing means for finer adjustments. Our empirical study demonstrates that verbal communication is indeed more robust to changes in stimuli dimensionality, but that its efficiency is adversely affected when distinguishing between categories requires perceptual precision. Communicating through examples has a reversed pattern. We hope that both the proposed experimental paradigm and the computational model would facilitate further research into the relative roles of verbal and exemplar communication in category learning.

AB - Both examples and verbal explanations play an important role in learning new concepts and categories. At the same time, learning from verbal explanations is not accounted for in most category learning models, and is not studied in the traditional category learning paradigm. We propose a rational category communication model that formally describes the process of communicating a category structure using both verbal explanations and visual examples in a pedagogical setting. We build our model based on the assumption that verbal instructions are best suited for communication of crude constraints on a category structure, while exemplars complement it by providing means for finer adjustments. Our empirical study demonstrates that verbal communication is indeed more robust to changes in stimuli dimensionality, but that its efficiency is adversely affected when distinguishing between categories requires perceptual precision. Communicating through examples has a reversed pattern. We hope that both the proposed experimental paradigm and the computational model would facilitate further research into the relative roles of verbal and exemplar communication in category learning.

KW - categorization

KW - category learning

KW - computational modelling

KW - communication channels

KW - communication efficiency

UR - http://www.proceedings.com/50152.html

M3 - Conference contribution

SN - 9781510891555

SP - 2406

EP - 2412

BT - 41st Annual Meeting of the Cognitive Science Society (CogSci 2019)

PB - Curran Associates, Inc.

Y2 - 24 July 2019 through 27 July 2019

ER -

ID: 49675274