Combining Personality and Physiology to Investigate the Flow Experience in Virtual Reality Games

  • Lazaros Michailidis
  • Jesus Lucas Barcias
  • Fred Charles
  • Xun HeEmail author
  • Emili Balaguer-BallesterEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1033)


Immersive experiences are typically considered an indicator of successful game design. The ability to maintain the player’s focus and enjoyment in the game lies at the core of game mechanics. In this work, we used a custom virtual reality game aiming to induce flow, boredom and anxiety throughout specific instances in the game. We used self-reports of personality and flow in addition to physiological measures (heart rate variability) as a means of evaluating the game design. Results yielded a consistently high accuracy in the classification of low flow versus high flow conditions across multiple classifiers. Moreover, they suggested that the anticipated model-by-design was not necessarily consistent with the player’s subjective and objective data. Our approach lays promising groundwork for the automatic assessment of game design strategies and may help explain experiential variability across video game players.


Flow Immersion Virtual reality HRV Game design Tower Defense Classification 



The authors wish to thank Jeremy Hogan (Worldwide Studios, London), Fabio Capello (Sony Interactive Entertainment, London) and Charlie Hargood (Bournemouth University) for their guidance, and Bournemouth University, EPSRC, Centre for Digital Entertainment and Sony Interactive Entertainment for funding Mr. Michailidis’s studentship.


  1. 1.
    Csikszentmihalyi, M.: Flow: The Psychology of Optimal Experience. Harper&Row, New York (1990)Google Scholar
  2. 2.
    Ullén, F., et al.: Proneness for psychological flow in everyday life: associations with personality and intelligence. Pers. Individ. Differ. 52(2), 167–172 (2012)CrossRefGoogle Scholar
  3. 3.
    Bassi, M., Steca, P., Monzani, D., Greco, A., Delle Fave, A.: Personality and optimal experience in adolescence: implications for well-being and development. J. Happiness Stud. 15(4), 829–843 (2014)CrossRefGoogle Scholar
  4. 4.
    Ullén, F., Harmat, L., Theorell, T., Madison, G.: Flow and individual differences – a phenotypic analysis of data from more than 10,000 twin individuals. In: Harmat, L., Ørsted, Andersen F., Ullén, F., Wright, J., Sadlo, G. (eds.) Flow Experience, pp. 267–288. Springer, Cham (2016). Scholar
  5. 5.
    Heller, K., Bullerjahn, C., von Georgi, R.: The relationship between personality traits, flow-experience, and different aspects of practice behavior of amateur vocal students. Front. Psychol. 6, 1901 (2015)CrossRefGoogle Scholar
  6. 6.
    Gray, J.A., McNaughton, N.: The Neuropsychology of Anxiety: An Enquiry into the Functions of the Septohippocampal System, 2nd edn. Oxford University Press, Oxford (2000)Google Scholar
  7. 7.
    Liu, C.C.: A model for exploring players flow experience in online games. Inf. Technol. People 30(1), 139–162 (2017)CrossRefGoogle Scholar
  8. 8.
    Nacke, L., Lindley, C.A.: Flow and immersion in first-person shooters: measuring the player’s gameplay experience. In: Proceedings of the 2008 Conference on Future Play: Research, Play, Share, pp. 81–88. ACM (2008)Google Scholar
  9. 9.
    Nah, F.F.H., Eschenbrenner, B., Zeng, Q., Telaprolu, V.R., Sepehr, S.: Flow in gaming: literature synthesis and framework development. Int. J. Inf. Syst. Manag. 1(1–2), 83–124 (2014)Google Scholar
  10. 10.
    Sherry, J.L.: Flow and media enjoyment. Communication theory 14(4), 328–347 (2004)CrossRefGoogle Scholar
  11. 11.
    Denisova, A., Cairns, P.: Adaptation in digital games: the effect of challenge adjustment on player performance and experience. In: Proceedings of the 2015 Annual Symposium on Computer-Human Interaction in Play, pp. 97–101. ACMGoogle Scholar
  12. 12.
    Michailidis, L., Balaguer-Ballester, E., He, X.: Flow and immersion in video games: the aftermath of a conceptual challenge. Front. Psychol. 9, 1682 (2018). Scholar
  13. 13.
    McCraty, R., Shaffer, F.: Heart rate variability: new perspectives on physiological mechanisms, assessment of self-regulatory capacity, and health risk. Glob. Adv. Health Med. 4(1), 46–61 (2015)CrossRefGoogle Scholar
  14. 14.
    Shaffer, F., Ginsberg, J.P.: An overview of heart rate variability metrics and norms. Front. Publ. Health 5, 258 (2017)CrossRefGoogle Scholar
  15. 15.
    Peifer, C., Schulz, A., Schächinger, H., Baumann, N., Antoni, C.H.: The relation of flow-experience and physiological arousal under stress—can u shape it? J. Exp. Soc. Psychol. 53, 62–69 (2014)CrossRefGoogle Scholar
  16. 16.
    Chanel, G., Rebetez, C., Bétrancourt, M., Pun, T.: Boredom, engagement and anxiety as indicators for adaptation to difficulty in games. In: Proceedings of the 12th International Conference on Entertainment and Media in the Ubiquitous Era, pp. 13–17. ACM (2008)Google Scholar
  17. 17.
    Keller, J., Bless, H., Blomann, F., Kleinböhl, D.: Physiological aspects of flow experiences: skills-demand-compatibility effects on heart rate variability and salivary cortisol. J. Exp. Soc. Psychol. 47(4), 849–852 (2011)CrossRefGoogle Scholar
  18. 18.
    Gargiulo, G., et al.: On the Einthoven triangle: a critical analysis of the single rotating dipole hypothesis. Sensors 18(7), 2353 (2018)CrossRefGoogle Scholar
  19. 19.
    Jackson, S.A., Eklund, R.C.: Assessing flow in physical activity: the flow state scale–2 and dispositional flow scale–2. J. Sport Exerc. Psychol. 24(2), 133–150 (2002)CrossRefGoogle Scholar
  20. 20.
    John, O.P., Srivastava, S.: The big-five trait taxonomy: history, measurement, and theoretical perspectives. In: Pervin, L.A., John, O.P. (eds.) Handbook of Personality: Theory and Research, vol. 2, pp. 102–138. Guilford Press, New York (1999)Google Scholar
  21. 21.
    Avery, P., Togelius, J., Alistar, E., Van Leeuwen, R.P.: Computational intelligence and tower defence games. In: 2011 IEEE Congress of Evolutionary Computation (CEC), pp. 1084–1091. IEEE (2011)Google Scholar
  22. 22.
    Sutoyo, R., Winata, D., Oliviani, K., Supriyadi, D.M.: Dynamic difficulty adjustment in tower defence. Procedia Comput. Sci. 59, 435–444 (2015)CrossRefGoogle Scholar
  23. 23.
    Pan, J., Tompkins, W.J.: A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. 32(3), 230–236 (1985)CrossRefGoogle Scholar
  24. 24.
    Sedghamiz, H.: MATLAB implementation of Pan Tompkins ECG QRS detector (2014).
  25. 25.
    Malik, M., Camm, A.J.: Heart Rate Variability. Futura, New York (1995)Google Scholar
  26. 26.
    Vicente, J., Laguna, P., Bartra, A., Bailón, R.: Drowsiness detection using heart rate variability. Med. Biol. Eng. Compu. 54(6), 927–937 (2016)CrossRefGoogle Scholar
  27. 27.
    Quade, D.: Rank analysis of covariance. J. Am. Stat. Assoc. 62(320), 1187–1200 (1967)MathSciNetCrossRefGoogle Scholar
  28. 28.
    Grodal, T.: Video games and the pleasures of control. In: Media Entertainment: The Psychology of Its Appeal, pp. 197–213 (2000)Google Scholar
  29. 29.
    Kaye, L.K., Monk, R.L., Wall, H.J., Hamlin, I., Qureshi, A.W.: The effect of flow and context on in-vivo positive mood in digital gaming. Int. J. Hum. Comput. Stud. 110, 45–52 (2018). Scholar
  30. 30.
    Pallavicini, F., Pepe, A., Minissi, M.E.: Gaming in virtual reality: what changes in terms of usability, emotional response and sense of presence compared to non-immersive video games? Simul. Gaming (2019). Scholar
  31. 31.
    Juul, J.: Fear of failing? The many meanings of difficulty in video games. Video Game Theory Reader 2, 237–252 (2009)Google Scholar
  32. 32.
    Gilleade, K.M., Dix, A.: Using frustration in the design of adaptive videogames. In: Proceedings of the 2004 ACM SIGCHI International Conference on Advances in Computer Entertainment Technology, pp. 228–232. ACM (2004)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Bournemouth UniversityBournemouthUK
  2. 2.Sony Interactive EntertainmentLondonUK

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