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Decoding individual differences in STEM learning from functional MRI data
Traditional tests of concept knowledge generate scores to assess how well a learner understands a concept. Here, we investigated whether patterns of brain activity collected during a concept knowledge task could be used to compute a neural ‘score’ to complement traditional scores of an individual’s...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6497647/ https://www.ncbi.nlm.nih.gov/pubmed/31048694 http://dx.doi.org/10.1038/s41467-019-10053-y |
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author | Cetron, Joshua S. Connolly, Andrew C. Diamond, Solomon G. May, Vicki V. Haxby, James V. Kraemer, David J. M. |
author_facet | Cetron, Joshua S. Connolly, Andrew C. Diamond, Solomon G. May, Vicki V. Haxby, James V. Kraemer, David J. M. |
author_sort | Cetron, Joshua S. |
collection | PubMed |
description | Traditional tests of concept knowledge generate scores to assess how well a learner understands a concept. Here, we investigated whether patterns of brain activity collected during a concept knowledge task could be used to compute a neural ‘score’ to complement traditional scores of an individual’s conceptual understanding. Using a novel data-driven multivariate neuroimaging approach—informational network analysis—we successfully derived a neural score from patterns of activity across the brain that predicted individual differences in multiple concept knowledge tasks in the physics and engineering domain. These tasks include an fMRI paradigm, as well as two other previously validated concept inventories. The informational network score outperformed alternative neural scores computed using data-driven neuroimaging methods, including multivariate representational similarity analysis. This technique could be applied to quantify concept knowledge in a wide range of domains, including classroom-based education research, machine learning, and other areas of cognitive science. |
format | Online Article Text |
id | pubmed-6497647 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-64976472019-05-06 Decoding individual differences in STEM learning from functional MRI data Cetron, Joshua S. Connolly, Andrew C. Diamond, Solomon G. May, Vicki V. Haxby, James V. Kraemer, David J. M. Nat Commun Article Traditional tests of concept knowledge generate scores to assess how well a learner understands a concept. Here, we investigated whether patterns of brain activity collected during a concept knowledge task could be used to compute a neural ‘score’ to complement traditional scores of an individual’s conceptual understanding. Using a novel data-driven multivariate neuroimaging approach—informational network analysis—we successfully derived a neural score from patterns of activity across the brain that predicted individual differences in multiple concept knowledge tasks in the physics and engineering domain. These tasks include an fMRI paradigm, as well as two other previously validated concept inventories. The informational network score outperformed alternative neural scores computed using data-driven neuroimaging methods, including multivariate representational similarity analysis. This technique could be applied to quantify concept knowledge in a wide range of domains, including classroom-based education research, machine learning, and other areas of cognitive science. Nature Publishing Group UK 2019-05-02 /pmc/articles/PMC6497647/ /pubmed/31048694 http://dx.doi.org/10.1038/s41467-019-10053-y Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Cetron, Joshua S. Connolly, Andrew C. Diamond, Solomon G. May, Vicki V. Haxby, James V. Kraemer, David J. M. Decoding individual differences in STEM learning from functional MRI data |
title | Decoding individual differences in STEM learning from functional MRI data |
title_full | Decoding individual differences in STEM learning from functional MRI data |
title_fullStr | Decoding individual differences in STEM learning from functional MRI data |
title_full_unstemmed | Decoding individual differences in STEM learning from functional MRI data |
title_short | Decoding individual differences in STEM learning from functional MRI data |
title_sort | decoding individual differences in stem learning from functional mri data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6497647/ https://www.ncbi.nlm.nih.gov/pubmed/31048694 http://dx.doi.org/10.1038/s41467-019-10053-y |
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