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.
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References
Granic, I., Lobel, A., Engels, R.C.M.E.: The benefits of playing video games. Am. Psychol. 69(1), 66–78 (2014)
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)
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)
Entertainment Software Association. Essential facts about the computer and video game industry: Entertainment Software Association, p. 11 (2016)
Bantinaki, K.: The paradox of horror: fear as a positive emotion. J. Aesthet. Art. Critic. 70(4), 383–392 (2012)
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)
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)
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)
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)
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)
Bartle, R.A.: Players who suit MUDs. Mud, p. 1 (1999)
Yee, N.: Motivations for play in online games. CyberPsychol. Behav. 9(6), 772–775 (2006)
Yannakakis, G.N., Hallam, J.: Real-time game adaptation for optimizing player satisfaction. IEEE Trans. Comput. Intell. AI Games 1(2), 121–133 (2009)
Pedersen, C.: Modeling player experience through super Mario Bros supervisor Georgios Yannakakis, Technology, pp. 132–139, August 2009
Fairclough, S.H.: Fundamentals of physiological computing. Interact. Comput. 21(1–2), 133–145 (2009)
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)
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)
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)
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)
Dehais, F., Causse, M., Vachon, F., Tremblay, S.: Cognitive conflict in human-automation interactions: a psychophysiological study. Appl. Ergonomics 43(3), 588–595 (2012)
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)
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)
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)
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)
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)
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)
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)
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)
Yannakakis, G.N., Martínez, H.P.: Ratings are overrated!. Frontiers ICT 2(7), 5 (2015)
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)
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)
Chen, T., Guestrin, C.: XGBoost: reliable large-scale tree boosting system. arXiv, pp. 1–6 (2016)
Hastie, T., Tibshirani, R., Friedman, J.: The elements of statistical learning. Elements 1, 337–387 (2009)
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.
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Fortin-Côté, A. et al. (2019). Predicting Video Game Players’ Fun from Physiological and Behavioural Data. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Advances in Information and Communication Networks. FICC 2018. Advances in Intelligent Systems and Computing, vol 886. Springer, Cham. https://doi.org/10.1007/978-3-030-03402-3_33
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