Stafford, T. and Dewar, M. (2013) Tracing the Trajectory of Skill Learning With a Very Large Sample of Online Game Players. Psychological Science, 25 (2). 511 - 518. ISSN 0956-7976
Abstract
In the present study, we analyzed data from a very large sample (N = 854,064) of players of an online game involving rapid perception, decision making, and motor responding. Use of game data allowed us to connect, for the first time, rich details of training history with measures of performance from participants engaged for a sustained amount of time in effortful practice. We showed that lawful relations exist between practice amount and subsequent performance, and between practice spacing and subsequent performance. Our methodology allowed an in situ confirmation of results long established in the experimental literature on skill acquisition. Additionally, we showed that greater initial variation in performance is linked to higher subsequent performance, a result we link to the exploration/exploitation trade-off from the computational framework of reinforcement learning. We discuss the benefits and opportunities of behavioral data sets with very large sample sizes and suggest that this approach could be particularly fecund for studies of skill acquisition.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Authors 2013. This is an author produced version of a paper subsequently published in Psychological Science. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | skill acquisition; learning; game; perceptual motor coordination |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > Department of Psychology (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 19 Mar 2015 12:41 |
Last Modified: | 22 Mar 2018 02:52 |
Published Version: | http://dx.doi.org/10.1177/0956797613511466 |
Status: | Published |
Publisher: | SAGE Publications |
Refereed: | Yes |
Identification Number: | 10.1177/0956797613511466 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:83582 |