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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2018
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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. |
format | Online Article Text |
id | pubmed-6297051 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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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