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Dataset of implicit sequence learning of chunking and abstract structures

This article describes the data analyzed in the paper “Implicit sequence learning of chunking and abstract structures” (Fu et al., 2018) [1]. It includes reaction times in the serial reaction time task and generation proformance for each confidence rating or attribution under the inclusion and exclu...

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Detalles Bibliográficos
Autores principales: Fu, Qiufang, Sun, Huiming, Dienes, Zoltán, Fu, Xiaolan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6297051/
https://www.ncbi.nlm.nih.gov/pubmed/30581907
http://dx.doi.org/10.1016/j.dib.2018.11.122
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author Fu, Qiufang
Sun, Huiming
Dienes, Zoltán
Fu, Xiaolan
author_facet Fu, Qiufang
Sun, Huiming
Dienes, Zoltán
Fu, Xiaolan
author_sort Fu, Qiufang
collection PubMed
description This article describes the data analyzed in the paper “Implicit sequence learning of chunking and abstract structures” (Fu et al., 2018) [1]. It includes reaction times in the serial reaction time task and generation proformance for each confidence rating or attribution under the inclusion and exclusion tests from three experiments. For the serial reaction time task, the independent varialbles were type of stimuli and blocks or type of deviants; for the generation tests, the independent varialbles were type of stimuli, instructions, and confidence ratings or attribution tests. The data can be used to examine wether a computor model can account for what type of knowledge is acquried in implicit sequence learning.
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spelling pubmed-62970512018-12-21 Dataset of implicit sequence learning of chunking and abstract structures Fu, Qiufang Sun, Huiming Dienes, Zoltán Fu, Xiaolan Data Brief Linguistics This article describes the data analyzed in the paper “Implicit sequence learning of chunking and abstract structures” (Fu et al., 2018) [1]. It includes reaction times in the serial reaction time task and generation proformance for each confidence rating or attribution under the inclusion and exclusion tests from three experiments. For the serial reaction time task, the independent varialbles were type of stimuli and blocks or type of deviants; for the generation tests, the independent varialbles were type of stimuli, instructions, and confidence ratings or attribution tests. The data can be used to examine wether a computor model can account for what type of knowledge is acquried in implicit sequence learning. Elsevier 2018-11-28 /pmc/articles/PMC6297051/ /pubmed/30581907 http://dx.doi.org/10.1016/j.dib.2018.11.122 Text en © 2018 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Linguistics
Fu, Qiufang
Sun, Huiming
Dienes, Zoltán
Fu, Xiaolan
Dataset of implicit sequence learning of chunking and abstract structures
title Dataset of implicit sequence learning of chunking and abstract structures
title_full Dataset of implicit sequence learning of chunking and abstract structures
title_fullStr Dataset of implicit sequence learning of chunking and abstract structures
title_full_unstemmed Dataset of implicit sequence learning of chunking and abstract structures
title_short Dataset of implicit sequence learning of chunking and abstract structures
title_sort dataset of implicit sequence learning of chunking and abstract structures
topic Linguistics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6297051/
https://www.ncbi.nlm.nih.gov/pubmed/30581907
http://dx.doi.org/10.1016/j.dib.2018.11.122
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