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Predicting Video Game Players’ Fun from Physiological and Behavioural Data

One Algorithm Does Not Fit All
  • Alexis Fortin-Côté
  • Cindy Chamberland
  • Mark Parent
  • Sébastien Tremblay
  • Philip Jackson
  • Nicolas Beaudoin-Gagnon
  • Alexandre Campeau-Lecours
  • Jérémy Bergeron-Boucher
  • Ludovic Lefebvre
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 886)

Abstract

Finding a physiological signature of a player’s fun is a goal yet to be achieved in the field of adaptive gaming. The research presented in this paper tackles this issue by gathering physiological, behavioural and self-report data from over 200 participants who played off-the-shelf video games from the Assassin’s Creed series within a minimally invasive laboratory environment. By leveraging machine learning techniques the prediction of the player’s fun from its physiological and behavioural markers becomes a possibility. They provide clues as to which signals are the most relevant in establishing a physiological signature of the fun factor by providing an important score based on the predictive power of each signal. Identifying those markers and their impact will prove crucial in the development of adaptive video games. Adaptive games tailor their gameplay to the affective state of a player in order to deliver the optimal gaming experience. Indeed, an adaptive video game needs a continuous reading of the fun level to be able to respond to these changing fun levels in real time. While the predictive power of the presented classifier remains limited with a gain in the F1 score of 15% against random chance, it brings insight as to which physiological features might be the most informative for further analysis and discuss means by which low accuracy classification could still improve gaming experience.

Keywords

Affective computing Machine learning Biomedical measurement Video game 

Notes

Acknowledgment

This project was funded by NSERC-CRSNG, Ubisoft Québec and Prompt. Additional thanks to Nvidia for providing a video card for deep learning analysis through their GPU Grant Program.

References

  1. 1.
    Granic, I., Lobel, A., Engels, R.C.M.E.: The benefits of playing video games. Am. Psychol. 69(1), 66–78 (2014)CrossRefGoogle Scholar
  2. 2.
    Djaouti, D., Alvarez, J., Jessel, J.-P.: Classifying serious games: the G/P/S model. In: Handbook of Research on Improving Learning and Motivation Through Educational Games: Multidisciplinary Approaches, vol. 2005, pp. 118–136 (2011)Google Scholar
  3. 3.
    Connolly, T.M., Boyle, E.A., MacArthur, E., Hainey, T., Boyle, J.M.: A systematic literature review of empirical evidence on computer games and serious games. Comput. Educ. 59(2), 661–686 (2012)CrossRefGoogle Scholar
  4. 4.
    Entertainment Software Association. Essential facts about the computer and video game industry: Entertainment Software Association, p. 11 (2016)Google Scholar
  5. 5.
    Bantinaki, K.: The paradox of horror: fear as a positive emotion. J. Aesthet. Art. Critic. 70(4), 383–392 (2012)CrossRefGoogle Scholar
  6. 6.
    Van Den Hoogen, W., Poels, K., IJsselsteijn, W., de Kort, Y.: Between challenge and defeat: repeated player-death and game enjoyment. Media Psychol. 15(4), 443–459 (2012)CrossRefGoogle Scholar
  7. 7.
    Mandryk, R.L., Inkpen, K.M., Calvert, T.W.: Using psychophysiological techniques to measure user experience with entertainment technologies. Behav. Inform. Technol. 25(2), 141–158 (2006)CrossRefGoogle Scholar
  8. 8.
    Zook, A.E., Riedl, M.O.: A temporal data-driven player model for dynamic difficulty adjustment. In: Proceedings of the 8th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2012, pp. 93–98 (2012)Google Scholar
  9. 9.
    Wirth, W., Ryffel, F., Von Pape, T., Karnowski, V.: The development of video game enjoyment in a role playing game. Cyberpsychol. Behav. Soc. Netw. 16(4), 260–264 (2013)CrossRefGoogle Scholar
  10. 10.
    Desmet, P.: Measuring emotion: development and application of an instrument to measure emotional responses to products. In: Funology: From Usability to Enjoyment, pp. 111–123 (2003)Google Scholar
  11. 11.
    Bartle, R.A.: Players who suit MUDs. Mud, p. 1 (1999)Google Scholar
  12. 12.
    Yee, N.: Motivations for play in online games. CyberPsychol. Behav. 9(6), 772–775 (2006)CrossRefGoogle Scholar
  13. 13.
    Yannakakis, G.N., Hallam, J.: Real-time game adaptation for optimizing player satisfaction. IEEE Trans. Comput. Intell. AI Games 1(2), 121–133 (2009)CrossRefGoogle Scholar
  14. 14.
    Pedersen, C.: Modeling player experience through super Mario Bros supervisor Georgios Yannakakis, Technology, pp. 132–139, August 2009Google Scholar
  15. 15.
    Fairclough, S.H.: Fundamentals of physiological computing. Interact. Comput. 21(1–2), 133–145 (2009)CrossRefGoogle Scholar
  16. 16.
    Cacioppo, J.T., Tassinary, L.G., Berntson, G.G.: Psychophysiological science: interdisciplinary approaches to classic questions about the mind. In: Handbook of Psychophysiology, pp. 3–22 (2000)Google Scholar
  17. 17.
    Robinson, M.D., Clore, G.L.: Belief and feeling: evidence for an accessibility model of emotional self-report. Psychol. Bull. 128(6), 934–960 (2002)CrossRefGoogle Scholar
  18. 18.
    Nacke, L.E.: An introduction to physiological player metrics for evaluating games. In: Seif El-Nasr, M., Drachen, A., Canossa, A. (eds.) Game Analytics, pp. 585–619. Springer, London (2013)CrossRefGoogle Scholar
  19. 19.
    Durantin, G., Gagnon, J.F., Tremblay, S., Dehais, F.: Using near infrared spectroscopy and heart rate variability to detect mental overload. Behav. Brain Res. 259, 16–23 (2014)CrossRefGoogle Scholar
  20. 20.
    Dehais, F., Causse, M., Vachon, F., Tremblay, S.: Cognitive conflict in human-automation interactions: a psychophysiological study. Appl. Ergonomics 43(3), 588–595 (2012)CrossRefGoogle Scholar
  21. 21.
    Rainville, P., Bechara, A., Naqvi, N., Damasio, A.R.: Basic emotions are associated with distinct patterns of cardiorespiratory activity. Int. J. Psychophysiol. 61(1), 5–18 (2006)CrossRefGoogle Scholar
  22. 22.
    Jang, E.-H., Park, B.-J., Park, M.-S., Kim, S.-H., Sohn, J.-H.: Analysis of physiological signals for recognition of boredom, pain, and surprise emotions. J. Physiol. Anthropol. 34, 1–12 (2015)CrossRefGoogle Scholar
  23. 23.
    Dekker, A., Champion, E.: Please Biofeed the Zombies: enhancing the gameplay and display of a horror game using biofeedback. In: Proceedings of DiGRA, pp. 550–558 (2007)Google Scholar
  24. 24.
    Emmen, D., Lampropoulos, G.: BioPong: adaptive gaming using biofeedback. In: Creating the Difference: Proceedings of the Chi Sparks 2014 Conference, no. 1, pp. 100–103 (2014)Google Scholar
  25. 25.
    Chamberland, C., Grégoire, M., Michon, P.-E., Gagnon, J.-C., Philip, L.: A cognitive and affective neuroergonomics approach to game design. In: 59th Annual Meeting of the Human Factors and Ergonomics Society, no. 2007, pp. 1075–1079 (2015)CrossRefGoogle Scholar
  26. 26.
    Clerico, A., Chamberland, C., Parent, M., Michon, P.-E., Tremblay, S., Falk, T.H., Gagnon, J.-C., Jackson, P.: Biometrics and classifier fusion to predict the fun-factor in video gaming. In: IEEE Conference on Computational Intelligence and Games (CIG 2016), pp. 233–240 (2016)Google Scholar
  27. 27.
    Jennett, C., Cox, A.L., Cairns, P., Dhoparee, S., Epps, A., Tijs, T., Walton, A.: Measuring and defining the experience of immersion in games. Int. J. Hum. Comput. Stud. 66(9), 641–661 (2008)CrossRefGoogle Scholar
  28. 28.
    Hart, S.G., Staveland, L.E.: Development of NASA-TLX (task load index): results of empirical and theoretical research. Adv. Psychol. 52, 139–183 (1988)CrossRefGoogle Scholar
  29. 29.
    Yannakakis, G.N., Martínez, H.P.: Ratings are overrated!. Frontiers ICT 2(7), 5 (2015)Google Scholar
  30. 30.
    Martinez, H.P., Yannakakis, G.N., Hallam, J.: Don’t classify ratings of affect; rank them!. IEEE Trans. Affect. Comput. 5(3), 314–326 (2014)CrossRefGoogle Scholar
  31. 31.
    Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetzbMATHGoogle Scholar
  32. 32.
    Chen, T., Guestrin, C.: XGBoost: reliable large-scale tree boosting system. arXiv, pp. 1–6 (2016)Google Scholar
  33. 33.
    Hastie, T., Tibshirani, R., Friedman, J.: The elements of statistical learning. Elements 1, 337–387 (2009)zbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Alexis Fortin-Côté
    • 1
  • Cindy Chamberland
    • 1
  • Mark Parent
    • 1
  • Sébastien Tremblay
    • 1
  • Philip Jackson
    • 1
  • Nicolas Beaudoin-Gagnon
    • 2
  • Alexandre Campeau-Lecours
    • 2
  • Jérémy Bergeron-Boucher
    • 3
  • Ludovic Lefebvre
    • 3
  1. 1.School of PsychologyUniversité LavalQuebecCanada
  2. 2.Department of Mechanical EngineeringUniversité LavalQuebecCanada
  3. 3.Ubisoft QuébecQuebecCanada

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