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  Five ways in which computational modeling can help advance cognitive science: Lessons from artificial grammar learning

Zuidema, W., French, R. M., Alhama, R. G., Ellis, K., O'Donnell, T. J. O., Sainburgh, T., et al. (2019). Five ways in which computational modeling can help advance cognitive science: Lessons from artificial grammar learning. Topics in Cognitive Science. Advance online publication. doi:10.1111/tops.12474.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0004-8959-E Version Permalink: http://hdl.handle.net/21.11116/0000-0005-1C3C-A
Genre: Journal Article

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Zuidema_et_al-2019-Topics_in_Cognitive_Science.pdf (Publisher version), 612KB
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Zuidema_et_al-2019-Topics_in_Cognitive_Science.pdf
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2019
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© 2019 The Authors. Topics in Cognitive Science published by Wiley Periodicals, Inc. on behalf of Cognitive Science Society This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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 Creators:
Zuidema, Willem1, Author
French, Robert M.2, Author
Alhama, Raquel G.3, Author              
Ellis, Kevin4, Author
O'Donnell, Timothy J. O.5, Author
Sainburgh, Tim6, Author
Gentner, Timothy Q.6, Author
Affiliations:
1University of Amsterdam, Amsterdam, The Netherlands, ou_persistent22              
2University of Burundy, Dijon, France, ou_persistent22              
3Language Development Department, MPI for Psycholinguistics, Max Planck Society, ou_2340691              
4Massachusetts Institute of Technology, Cambridge, MA, USA, ou_persistent22              
5McGill University, Montreal, Canada, ou_persistent22              
6University of California San Diego, San Diego, CA, USA, ou_persistent22              

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 Abstract: There is a rich tradition of building computational models in cognitive science, but modeling, theoretical, and experimental research are not as tightly integrated as they could be. In this paper, we show that computational techniques—even simple ones that are straightforward to use—can greatly facilitate designing, implementing, and analyzing experiments, and generally help lift research to a new level. We focus on the domain of artificial grammar learning, and we give five concrete examples in this domain for (a) formalizing and clarifying theories, (b) generating stimuli, (c) visualization, (d) model selection, and (e) exploring the hypothesis space.

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Language(s): eng - English
 Dates: 2019-082019-10-30
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1111/tops.12474
 Degree: -

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Title: Topics in Cognitive Science. Advance online publication
  Other : Top Cogn Sci
Source Genre: Journal
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Publ. Info: Oxford [u.a.] : Wiley-Blackwell
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: ISSN: 1756-8757
CoNE: https://pure.mpg.de/cone/journals/resource/1756-8757