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.
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Acknowledgments
This project is partially funded under the European Community Seventh Framework Programme (FP7/2007 2013), Grant Agreement nr. 258169 and EPSRC/IMRC grants 113946 and 112430.
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Hislop, M., Sivanathan, A., Lim, T., Ritchie, J.M., Rajendran, G., Louchart, S. (2014). Lecture Notes in Computer Science: Beyond simulators, Using F1 Games to Predict Driver Performance, Learning and Potential. In: De Gloria, A. (eds) Games and Learning Alliance. GALA 2013. Lecture Notes in Computer Science(), vol 8605. Springer, Cham. https://doi.org/10.1007/978-3-319-12157-4_13
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DOI: https://doi.org/10.1007/978-3-319-12157-4_13
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