Examining the Degree of Engagement of a Participant in Economic Games Using Cognitive Neuroscience Techniques

  • Konrad BiercewiczEmail author
  • Mariusz Borawski
Conference paper
Part of the Springer Proceedings in Business and Economics book series (SPBE)


The popularity of economic games is used mainly for learning. It causes a need for methods allowing to evaluate their content in terms of player’s satisfaction already at the pre-production stage. There are already methods to determine a player’s interest in a game, but they do not always allow for objective and unambiguous determination of a player’s involvement. Cognitive neuroscience methods can give such an assessment. The aim of the research presented in this chapter is to develop a concept of a procedure for investigating a player’s involvement in a game using cognitive neuroscience methods. The chapter presents the concept of the research procedure, the survey, the prototype of the game and the review of the engagement indexes. On the basis of the analysis of the results of the survey, it was stated, among others, that the respondents do not like to take a big risk related to money. Therefore, in the designed economy game, the player should be accustomed to taking risks in order not to be discouraged from the game. In addition, through the use of cognitive neuroscience, we are able to have knowledge of the level of engagement of the player in each part of the game. Then, game developers will be able to improve them in order to get the greatest satisfaction from the player. In the case of economic games, this will translate into a longer time spent by the player on the game, and thus his skills acquired during the game will be greater.


Economic game Cognitive neuroscience EEG Engagement 


  1. 1.
    Latuszyńska, M.: Experimental research in economics and computer simulation. In: Nermend, K., Latuszyńska, M. (eds.) Selected Issues in Experimental Economics, pp. 151–169. Springer, Cham (2016)CrossRefGoogle Scholar
  2. 2.
    Martey, R.M., Kenski, K., Folkestad, J., et al.: Measuring game engagement: Multiple methods and construct complexity. Simul. Gaming 45(4–5), 528–547 (2014). Scholar
  3. 3.
    Chiang, Y., Cheng, C., Lin, S.S.J.: The effects of digital games on undergraduate players’ flow experiences and affect. In: 2008 Second IEEE International Conference on Digital Game and Intelligent Toy Enhanced Learning, pp. 157–159 (2008).
  4. 4.
    Witmer, A., Slater, M.: Measuring presence: A response to the Witmer and singer presence questionnaire. Presence Virtual Augmented Reality 8(5), 560–565 (1999). Scholar
  5. 5.
    Hosťovecký, M., Babusiak, B.: Brain activity: Beta wave analysis of 2D and 3D serious games using EEG. J. Appl. Math. Stat. Inform. 13(6), 39–53 (2018). Scholar
  6. 6.
    Plass-Oude Bos, D., Reuderink, B., van de Laar, B. et al.: Brain-computer interfacing and games. In: Brain-Computer Interfaces: Applying Our Minds to Human-Computer Interaction, pp. 149–178. (2010). Scholar
  7. 7.
    Pope, A.T., Bogart, E.H., Bartolome, D.S.: Biocybernetic system evaluates indices of operator engagement in automated task. Biol. Psychol. 40(1–2), 187–195 (1995). Scholar
  8. 8.
    Smith, M., Gevins, A.: Neurophysiologic monitoring of mental workload and fatigue during operation of a flight simulator. Proc. SPIE Biomonitoring Physiol. Cogn. Perform. During Mil. Oper. 5797, 116–126 (2005). Scholar
  9. 9.
    Yamada, F.: Frontal midline theta rhythm and eye blinking activity during a VDT task and a video game: Useful tools for psychophysiology in ergonomics. Ergonomics 41(5), 678–688 (1998). Scholar
  10. 10.
    Hockey, G.R.J., Nickel, P., Roberts, A.C., Roberts, M.H.: Sensitivity of candidate markers of psychophysiological strain to cyclical changes in manual control load during simulated process control. Appl. Ergonomics 40(6), 1011–1018 (2009). Scholar
  11. 11.
    Nassef, A., Mahfouf, M., Linkens, D.A., et al.: The assessment of heart rate variability (HRV) and task load index (TLI) as physiological markers for physical stress. In: Dössel, O., Schlegel, W.C. (eds.) World Congress on Medical Physics and Biomedical Engineering, September 2009, Munich, Germany, pp. 146–149. Springer, Heidelberg (2010)Google Scholar
  12. 12.
    McMahan, T., Parberry, I., Parsons, T.D.: Evaluating player task engagement and arousal using electroencephalography. In: 6th International Conference on Applied Human Factors and Ergonomics (AHFE 2015) and the Affiliated Conferences vol. 3, pp. 2303–2310. (2015). Scholar
  13. 13.
    Kamzanova, A.T., Matthews, G., Kustubayeva, A.M., Jakupov, S.M.: EEG indices to time-on-task effects and to a workload manipulation (Cueing). World Acad. Sci. Eng. Technol. 80, 19–22 (2011)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Multimedia Systems, Faculty of Computer Science and Information TechnologyWest Pomeranian University of Technology SzczecinSzczecinPoland

Personalised recommendations