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 proceedingChapter (peer-reviewed)peer-review


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 languageEnglish
Title of host publicationGames and Learning Alliance
Subtitle of host publicationSecond International Conference, GALA 2013, Paris, France, October 23-25, 2013, Revised Selected Papers
Number of pages15
ISBN (Electronic)978-3-319-12157-4
ISBN (Print)9783319121567
Publication statusPublished - 2014
EventSecond International Games and Learning Alliance Conference - Dassault Systemes, Paris, France
Duration: 23 Oct 201325 Oct 2013

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)16113349


ConferenceSecond International Games and Learning Alliance Conference
Abbreviated titleGALA 2013


  • Learning
  • Skills acquisition
  • Behavioural correlates
  • Cross-modal distraction
  • Dreyfus paradigm


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