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Video Game Telemetry as a Critical Tool in the Study of Complex Skill Learning

Cognitive science has long shown interest in expertise, in part because prediction and control of expert development would have immense practical value. Most studies in this area investigate expertise by comparing experts with novices. The reliance on contrastive samples in studies of human expertis...

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Autores principales: Thompson, Joseph J., Blair, Mark R., Chen, Lihan, Henrey, Andrew J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3776738/
https://www.ncbi.nlm.nih.gov/pubmed/24058656
http://dx.doi.org/10.1371/journal.pone.0075129
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author Thompson, Joseph J.
Blair, Mark R.
Chen, Lihan
Henrey, Andrew J.
author_facet Thompson, Joseph J.
Blair, Mark R.
Chen, Lihan
Henrey, Andrew J.
author_sort Thompson, Joseph J.
collection PubMed
description Cognitive science has long shown interest in expertise, in part because prediction and control of expert development would have immense practical value. Most studies in this area investigate expertise by comparing experts with novices. The reliance on contrastive samples in studies of human expertise only yields deep insight into development where differences are important throughout skill acquisition. This reliance may be pernicious where the predictive importance of variables is not constant across levels of expertise. Before the development of sophisticated machine learning tools for data mining larger samples, and indeed, before such samples were available, it was difficult to test the implicit assumption of static variable importance in expertise development. To investigate if this reliance may have imposed critical restrictions on the understanding of complex skill development, we adopted an alternative method, the online acquisition of telemetry data from a common daily activity for many: video gaming. Using measures of cognitive-motor, attentional, and perceptual processing extracted from game data from 3360 Real-Time Strategy players at 7 different levels of expertise, we identified 12 variables relevant to expertise. We show that the static variable importance assumption is false - the predictive importance of these variables shifted as the levels of expertise increased - and, at least in our dataset, that a contrastive approach would have been misleading. The finding that variable importance is not static across levels of expertise suggests that large, diverse datasets of sustained cognitive-motor performance are crucial for an understanding of expertise in real-world contexts. We also identify plausible cognitive markers of expertise.
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spelling pubmed-37767382013-09-20 Video Game Telemetry as a Critical Tool in the Study of Complex Skill Learning Thompson, Joseph J. Blair, Mark R. Chen, Lihan Henrey, Andrew J. PLoS One Research Article Cognitive science has long shown interest in expertise, in part because prediction and control of expert development would have immense practical value. Most studies in this area investigate expertise by comparing experts with novices. The reliance on contrastive samples in studies of human expertise only yields deep insight into development where differences are important throughout skill acquisition. This reliance may be pernicious where the predictive importance of variables is not constant across levels of expertise. Before the development of sophisticated machine learning tools for data mining larger samples, and indeed, before such samples were available, it was difficult to test the implicit assumption of static variable importance in expertise development. To investigate if this reliance may have imposed critical restrictions on the understanding of complex skill development, we adopted an alternative method, the online acquisition of telemetry data from a common daily activity for many: video gaming. Using measures of cognitive-motor, attentional, and perceptual processing extracted from game data from 3360 Real-Time Strategy players at 7 different levels of expertise, we identified 12 variables relevant to expertise. We show that the static variable importance assumption is false - the predictive importance of these variables shifted as the levels of expertise increased - and, at least in our dataset, that a contrastive approach would have been misleading. The finding that variable importance is not static across levels of expertise suggests that large, diverse datasets of sustained cognitive-motor performance are crucial for an understanding of expertise in real-world contexts. We also identify plausible cognitive markers of expertise. Public Library of Science 2013-09-18 /pmc/articles/PMC3776738/ /pubmed/24058656 http://dx.doi.org/10.1371/journal.pone.0075129 Text en © 2013 Thompson et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Thompson, Joseph J.
Blair, Mark R.
Chen, Lihan
Henrey, Andrew J.
Video Game Telemetry as a Critical Tool in the Study of Complex Skill Learning
title Video Game Telemetry as a Critical Tool in the Study of Complex Skill Learning
title_full Video Game Telemetry as a Critical Tool in the Study of Complex Skill Learning
title_fullStr Video Game Telemetry as a Critical Tool in the Study of Complex Skill Learning
title_full_unstemmed Video Game Telemetry as a Critical Tool in the Study of Complex Skill Learning
title_short Video Game Telemetry as a Critical Tool in the Study of Complex Skill Learning
title_sort video game telemetry as a critical tool in the study of complex skill learning
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3776738/
https://www.ncbi.nlm.nih.gov/pubmed/24058656
http://dx.doi.org/10.1371/journal.pone.0075129
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