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Finding the traces of behavioral and cognitive processes in big data and naturally occurring datasets

Today, people generate and store more data than ever before as they interact with both real and virtual environments. These digital traces of behavior and cognition offer cognitive scientists and psychologists an unprecedented opportunity to test theories outside the laboratory. Despite general exci...

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Detalles Bibliográficos
Autores principales: Paxton, Alexandra, Griffiths, Thomas L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5628193/
https://www.ncbi.nlm.nih.gov/pubmed/28425058
http://dx.doi.org/10.3758/s13428-017-0874-x
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author Paxton, Alexandra
Griffiths, Thomas L.
author_facet Paxton, Alexandra
Griffiths, Thomas L.
author_sort Paxton, Alexandra
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description Today, people generate and store more data than ever before as they interact with both real and virtual environments. These digital traces of behavior and cognition offer cognitive scientists and psychologists an unprecedented opportunity to test theories outside the laboratory. Despite general excitement about big data and naturally occurring datasets among researchers, three “gaps” stand in the way of their wider adoption in theory-driven research: the imagination gap, the skills gap, and the culture gap. We outline an approach to bridging these three gaps while respecting our responsibilities to the public as participants in and consumers of the resulting research. To that end, we introduce Data on the Mind (http://www.dataonthemind.org), a community-focused initiative aimed at meeting the unprecedented challenges and opportunities of theory-driven research with big data and naturally occurring datasets. We argue that big data and naturally occurring datasets are most powerfully used to supplement—not supplant—traditional experimental paradigms in order to understand human behavior and cognition, and we highlight emerging ethical issues related to the collection, sharing, and use of these powerful datasets.
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spelling pubmed-56281932017-10-17 Finding the traces of behavioral and cognitive processes in big data and naturally occurring datasets Paxton, Alexandra Griffiths, Thomas L. Behav Res Methods Article Today, people generate and store more data than ever before as they interact with both real and virtual environments. These digital traces of behavior and cognition offer cognitive scientists and psychologists an unprecedented opportunity to test theories outside the laboratory. Despite general excitement about big data and naturally occurring datasets among researchers, three “gaps” stand in the way of their wider adoption in theory-driven research: the imagination gap, the skills gap, and the culture gap. We outline an approach to bridging these three gaps while respecting our responsibilities to the public as participants in and consumers of the resulting research. To that end, we introduce Data on the Mind (http://www.dataonthemind.org), a community-focused initiative aimed at meeting the unprecedented challenges and opportunities of theory-driven research with big data and naturally occurring datasets. We argue that big data and naturally occurring datasets are most powerfully used to supplement—not supplant—traditional experimental paradigms in order to understand human behavior and cognition, and we highlight emerging ethical issues related to the collection, sharing, and use of these powerful datasets. Springer US 2017-04-19 2017 /pmc/articles/PMC5628193/ /pubmed/28425058 http://dx.doi.org/10.3758/s13428-017-0874-x Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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.
spellingShingle Article
Paxton, Alexandra
Griffiths, Thomas L.
Finding the traces of behavioral and cognitive processes in big data and naturally occurring datasets
title Finding the traces of behavioral and cognitive processes in big data and naturally occurring datasets
title_full Finding the traces of behavioral and cognitive processes in big data and naturally occurring datasets
title_fullStr Finding the traces of behavioral and cognitive processes in big data and naturally occurring datasets
title_full_unstemmed Finding the traces of behavioral and cognitive processes in big data and naturally occurring datasets
title_short Finding the traces of behavioral and cognitive processes in big data and naturally occurring datasets
title_sort finding the traces of behavioral and cognitive processes in big data and naturally occurring datasets
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5628193/
https://www.ncbi.nlm.nih.gov/pubmed/28425058
http://dx.doi.org/10.3758/s13428-017-0874-x
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