Beyond simulators, using F1 games to predict driver performance, learning and potential

Matthew Hislop; Aparajithan Sivanathan; Theodore Lim; James M. Ritchie; Gnanathusharan Rajendran; Sandy Louchart

doi:10.1007/978-3-319-12157-4_13

Beyond simulators, using F1 games to predict driver performance, learning and potential

Matthew Hislop, Aparajithan Sivanathan, Theodore Lim^*, James M. Ritchie, Gnanathusharan Rajendran, Sandy Louchart

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed) › peer-review

Abstract

Formula One (F1) drivers are amongst the most highly skilled drivers in the world, but not every F1 driver is destined to be a F1 World Champion. Discovering new talent or refreshing strategies are long-term investments for all competitive F1 teams. The F1 world and teams invest vast amounts in developing high-fidelity simulators; however, driving games have seldom been associated with uncovering certain natural abilities. Beyond nature and nurture to attain success at the top level, certain motor-cognitive aspects are paramount for proficiency. One method of potentially finding talent is studying the behavioral and cognitive patterns associated with learning. Here, an F1 simulation game was used to demonstrate how learning had taken place. The indicative change of interest is from cognitive to motor via more skilled autonomous driving style –a skill synonymous with expert driving and ultimately winning races. Our data show clear patterns of how this skill develops.

Original language	English
Title of host publication	Games and Learning Alliance
Subtitle of host publication	Second International Conference, GALA 2013, Paris, France, October 23-25, 2013, Revised Selected Papers
Publisher	Springer
Pages	157-171
Number of pages	15
Volume	8605
ISBN (Electronic)	978-3-319-12157-4
ISBN (Print)	9783319121567
DOIs	https://doi.org/10.1007/978-3-319-12157-4_13
Publication status	Published - 2014
Event	Second International Games and Learning Alliance Conference - Dassault Systemes, Paris, France Duration: 23 Oct 2013 → 25 Oct 2013

Publication series

Name	Lecture Notes in Computer Science
Volume	8605
ISSN (Print)	0302-9743
ISSN (Electronic)	16113349

Conference

Conference	Second International Games and Learning Alliance Conference
Abbreviated title	GALA 2013
Country/Territory	France
City	Paris
Period	23/10/13 → 25/10/13

Keywords

Learning
Skills acquisition
Behavioural correlates
Cross-modal distraction
Dreyfus paradigm

Access to Document

10.1007/978-3-319-12157-4_13

https://link.springer.com/chapter/10.1007/978-3-319-12157-4_13

Cite this

Hislop, M., Sivanathan, A., Lim, T., Ritchie, J. M., Rajendran, G., & Louchart, S. (2014). Beyond simulators, using F1 games to predict driver performance, learning and potential. In Games and Learning Alliance: Second International Conference, GALA 2013, Paris, France, October 23-25, 2013, Revised Selected Papers (Vol. 8605, pp. 157-171). (Lecture Notes in Computer Science; Vol. 8605). Springer. https://doi.org/10.1007/978-3-319-12157-4_13

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abstract = "Formula One (F1) drivers are amongst the most highly skilled drivers in the world, but not every F1 driver is destined to be a F1 World Champion. Discovering new talent or refreshing strategies are long-term investments for all competitive F1 teams. The F1 world and teams invest vast amounts in developing high-fidelity simulators; however, driving games have seldom been associated with uncovering certain natural abilities. Beyond nature and nurture to attain success at the top level, certain motor-cognitive aspects are paramount for proficiency. One method of potentially finding talent is studying the behavioral and cognitive patterns associated with learning. Here, an F1 simulation game was used to demonstrate how learning had taken place. The indicative change of interest is from cognitive to motor via more skilled autonomous driving style –a skill synonymous with expert driving and ultimately winning races. Our data show clear patterns of how this skill develops.",

keywords = "Learning, Skills acquisition, Behavioural correlates, Cross-modal distraction, Dreyfus paradigm",

author = "Matthew Hislop and Aparajithan Sivanathan and Theodore Lim and Ritchie, {James M.} and Gnanathusharan Rajendran and Sandy Louchart",

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Hislop, M, Sivanathan, A, Lim, T, Ritchie, JM, Rajendran, G & Louchart, S 2014, Beyond simulators, using F1 games to predict driver performance, learning and potential. in Games and Learning Alliance: Second International Conference, GALA 2013, Paris, France, October 23-25, 2013, Revised Selected Papers. vol. 8605, Lecture Notes in Computer Science, vol. 8605, Springer, pp. 157-171, Second International Games and Learning Alliance Conference, Paris, France, 23/10/13. https://doi.org/10.1007/978-3-319-12157-4_13

Beyond simulators, using F1 games to predict driver performance, learning and potential. / Hislop, Matthew; Sivanathan, Aparajithan; Lim, Theodore et al.
Games and Learning Alliance: Second International Conference, GALA 2013, Paris, France, October 23-25, 2013, Revised Selected Papers. Vol. 8605 Springer, 2014. p. 157-171 (Lecture Notes in Computer Science; Vol. 8605).

Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed) › peer-review

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AU - Hislop, Matthew

AU - Sivanathan, Aparajithan

AU - Lim, Theodore

AU - Ritchie, James M.

AU - Rajendran, Gnanathusharan

AU - Louchart, Sandy

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AB - Formula One (F1) drivers are amongst the most highly skilled drivers in the world, but not every F1 driver is destined to be a F1 World Champion. Discovering new talent or refreshing strategies are long-term investments for all competitive F1 teams. The F1 world and teams invest vast amounts in developing high-fidelity simulators; however, driving games have seldom been associated with uncovering certain natural abilities. Beyond nature and nurture to attain success at the top level, certain motor-cognitive aspects are paramount for proficiency. One method of potentially finding talent is studying the behavioral and cognitive patterns associated with learning. Here, an F1 simulation game was used to demonstrate how learning had taken place. The indicative change of interest is from cognitive to motor via more skilled autonomous driving style –a skill synonymous with expert driving and ultimately winning races. Our data show clear patterns of how this skill develops.

KW - Learning

KW - Skills acquisition

KW - Behavioural correlates

KW - Cross-modal distraction

KW - Dreyfus paradigm

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ER -

Hislop M, Sivanathan A, Lim T, Ritchie JM, Rajendran G, Louchart S. Beyond simulators, using F1 games to predict driver performance, learning and potential. In Games and Learning Alliance: Second International Conference, GALA 2013, Paris, France, October 23-25, 2013, Revised Selected Papers. Vol. 8605. Springer. 2014. p. 157-171. (Lecture Notes in Computer Science). doi: 10.1007/978-3-319-12157-4_13

Beyond simulators, using F1 games to predict driver performance, learning and potential

Abstract

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Keywords

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