DETERMINING THE DEGREE OF PLAYER ENGAGEMENT IN A COMPUTER GAME WITH ELEMENTS OF A SOCIAL CAMPAIGN USING COGNITIVE NEUROSCIENCE TECHNIQUES
Konrad BIERCEWICZ
konrad.biercewicz@usz.edu.plFinance and Management Faculty of Economics, University of Szczecin, Szczecin (Poland)
Mariusz BORAWSKI
Finance and Management Faculty of Economics, University of Szczecin, Szczecin (Poland)
Anna BORAWSKA
Finance and Management Faculty of Economics, University of Szczecin, Szczecin (Poland)
Jarosław DUDA
Finance and Management Faculty of Economics, University of Szczecin, Szczecin (Poland)
Abstract
Due to the popularity of video games in various applications, including both commercial and social marketing, there is a need to assess their content in terms of player satisfaction, already at the production stage. For this purpose, the indices used in EEG tests can be used. In this publication, a formula has been created based on the player's commitment to determining which elements in the game should be improved and for which graphic emblems connected with social campaigns were more memorable and whether this was related to commitment. The survey was conducted using a 2D platform game created in Unity based on observations of 28 recipients. To evaluate the elements occurring in the game at which we obtain a higher memory for graphic characters, a corresponding pattern was created based on player involvement. The optimal Index for moving and static objects and the Index for destruction were then selected based on the feedback. Referring to the issue of graphic emblems depicting social campaigns should be placed in a place where other activities such as fighting will not be distracted, everyone will be able to reach the level where the recently placed advertisement is. This study present the developed method to determine the degree of player's engagement in particular elements in the game using the EEG and to explore the relationship between the visibility of social advertising and engagement in a 2D platform game where the player has to collect three keys and defeat the ultimate opponent.
Keywords:
EEG, Eye Tracker, engagement, IGA, AdvergamingReferences
D Platformer—Asset Store. (b.d.).Unity Asset Store. Retrieved September 2, 2019 from https://assetstore.unity.com/packages/essentials/tutorial-projects/2d-platformer-11228
Google Scholar
Berka, C., Levendowski, D. J., Lumicao, M. N., Yau, A., Davis, G., Zivkovic, V. T., Olmstead, R. E., Tremoulet, P. D., & Craven, P. L. (2007). EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks. Aviation, Space, and Environmental Medicine, 78(5 Suppl), B231–244.
Google Scholar
Brown, E., & Cairns, P. (2004). A Grounded Investigation of Game Immersion. CHI ’04 Extended Abstracts on Human Factors in Computing Systems(pp. 1297–1300). The ACM Digital Library. https://doi.org/10.1145/985921.986048
Google Scholar
Chang, Y., Yan, J., Zhang, J., & Luo, J. (2010). Online In-Game Advertising Effect: Examining the Influence of a Match Between Games and Advertising. Journal of Interactive Advertising, 11, 63–73. https://doi.org/10.1080/15252019.2010.10722178
Google Scholar
Chaouachi, M., & Frasson, C. (2012). Mental Workload, Engagement and Emotions: An Exploratory Study for Intelligent Tutoring Systems. Intelligent Tutoring Systems. ITS 2012. Lecture Notes in Computer Science(vol 7315). Springer Heidelberg. https://doi.org/10.1007/978-3-642-30950-2_9
Google Scholar
Chen, J. (2007). Flow in Games (and Everythingelse). Commun. ACM, 50(4), 31–34. https://doi.org/10.1145/1232743.1232769
Google Scholar
Costello, B., & Edmonds, E. (2009). A Tool for Characterizing the Experience of Play. Proceedings of the Sixth Australasian Conference on Interactive Entertainment, 2, 1–10. https://doi.org/10.1145/1746050.1746052
Google Scholar
Csikszentmihalyi, M. (1990). Flow: The Psychology of Optimal Experience.Harper Perennial Modern Classics.
Google Scholar
Ewing, K. C., Fairclough, S. H., & Gilleade, K. (2016). Evaluation of an Adaptive Game that Uses EEG Measures Validated during the Design Process as Inputs to a Biocybernetic Loop. Frontiers in Human Neuroscience, 10, 223–223. https://doi.org/10.3389/fnhum.2016.00223
Google Scholar
Filsecker, M., & Kerres, M. (2014). Engagement as a Volitional Construct: A Framework for Evidence-Based Research on Educational Games. Simulation & Gaming, 45(4–5), 450–470. https://doi.org/10.1177/1046878114553569
Google Scholar
Freeman, F. G., Mikulka, P. J., Prinzel, L. J., &Scerbo, M. W. (1999). Evaluation of an adaptive automation system using three EEG indices with a visual tracking task. Biological Psychology, 50(1), 61–76.
Google Scholar
Gałuszka, D. (2016). Nowy wymiar reklamy-in-game advertising oraz advergaming. Kultura i Historia, 29, 33–49.GameAnalytics. (n.d.).
Google Scholar
GameAnalytics. Retrieved April 5, 2020 from https://gameanalytics.com/
Google Scholar
Hofman-Kohlmeyer, M. (2017). Komunikacja marketingowa w grach komputerowych—Współczesne kierunki badań. Studia Ekonomiczne, 328, 70–82.
Google Scholar
Hondrou, C., &Caridakis, G. (2012). Affective, Natural Interaction Using EEG: Sensors, Application and Future Directions. W I. Maglogiannis, V. Plagianakos, & I. Vlahavas (Eds.), Artificial Intelligence: Theories and Applications(s. 331–338). Springer Berlin Heidelberg.
Google Scholar
Hyman, P. (2007). Burger King has it their way with advergame sales. Hollywood Report.
Google Scholar
Iacovides, I., Aczel, J., Scanlon, E., Taylor, J., & Woods, W. (2011). Motivation, Engagement and Learning through Digital Games. IJVPLE, 2, 1–16. https://doi.org/10.4018/jvple.2011040101
Google Scholar
Ijsselsteijn, W., van den Hoogen, W., Klimmt, C., De Kort, Y., Lindley, C., Mathiak, K., Poels, K., Ravaja, N., Turpeinen, M., & Vorderer, P. (2008). Measuring the experience of digital game enjoyment. Proceedings of Measuring Behavior(pp. 88–89). Noldus.
Google Scholar
Jennett, C., Cox, A. L., Cairns, P., Dhoparee, S., Epps, A., Tijs, T., & Walton, A. (2008). Measuring and defining the experience of immersion in games. International Journal of Human-Computer Studies, 66(9), 641–661. https://doi.org/10.1016/j.ijhcs.2008.04.004
Google Scholar
Jurcak, V., Tsuzuki, D., & Dan, I. (2007). 10/20, 10/10, and 10/5 systems revisited: Their validity as relative head-surface-based positioning systems. NeuroImage, 34(4), 1600–1611. https://doi.org/10.1016/j.neuroimage.2006.09.024
Google Scholar
Kamzanova, A. T., Matthews, G., Kustubayeva,A. M., & Jakupov, S. M. (2011). EEG indices to time-on-task effects and to a workload manipulation (Cueing). World Academy of Science, Engineering and Technology, 80, 19–22.
Google Scholar
Kiedy reklama flirtuje z gamingiem... (2015, March20). Marketer+ przewodnik po marketingu. https://marketerplus.pl/teksty/artykuly/reklama-flirtuje-gamingiem/
Google Scholar
Koster, R., & Wright,W. (2004). A Theory of Fun for Game Design. Paraglyph Press.
Google Scholar
Lee, C., Kwon, J., Hong, J., & Lee, D. (2010). A Study on EEG based Concentration Power Index Transmission and Brain Computer Interface Application(s. 537–539). Springer. https://doi.org/10.1007/978-3-642-03882-2_142
Google Scholar
Lilly, J. M., & Olhede, S. C. (2010). On the Analytic Wavelet Transform. IEEE Transactions on Information Theory, 56(8), 4135–4156. https://doi.org/10.1109/TIT.2010.2050935
Google Scholar
Lilly, J.M., & Olhede, S.C.(2012). Generalized Morse Wavelets as a Superfamily of Analytic Wavelets. IEEE Transactionson Signal Processing, 60(11), 6036-6041. https://doi.org/10.1109/TSP.2012.2210890
Google Scholar
Lokowanie produktów w grach komputerowych—Prawo własności intelektualnej. (b.d.). Retrieved April 3, 2020 from https://www.pwi.us.edu.pl/kategorie/prawo-reklamy/253-lokowanie-produktow-w-grach-komputerowych?jjj=1586621566826&jjj=1586626966769
Google Scholar
Lombard, M., & Ditton, T. (1997). At the heart of it all: The concept of presence. Journal of Computer-Mediated Communication, 3(2).
Google Scholar
Mcmahan, A. (2003). Immersion, engagement, and presence: A method for analyzing 3-D video games. The Video Game Theory Reader(pp. 67–86).Routledge, Taylor & Francis Group.
Google Scholar
McMahan, T., Parberry, I., & Parsons, T. D. (2015). Evaluating Player Task Engagement and Arousal Using Electroencephalography. Procedia Manufacturing, 3, 2303–2310. https://doi.org/10.1016/j.promfg.2015.07.376
Google Scholar
Pope, A.T., Bogart, E. H., & Bartolome, D. S. (1995). Biocybernetic system evaluates indices of operator engagement in automated task. Biological Psychology, 40(1–2), 187–195. https://doi.org/10.1016/0301-0511(95)05116-3
Google Scholar
Przybylski, A.K., Scott Rigby, C., & Ryan, R. (2010). A Motivational Model of Video Game Engagement. Review of General Psychology, 14, 154–166. https://doi.org/10.1037/a0019440
Google Scholar
Ravaja, N., Saari, T., Salminen, M., Laarni, J., & Kallinen, K. (2006). Phasic Emotional Reactions to Video Game Events: A Psychophysiological Investigation. Media Psychology, 8(4), 343–367. https://doi.org/10.1207/s1532785xmep0804_2
Google Scholar
Rubin, B. (b.d.). Policies & Programs to Reduce Distracted Driving. Retrieved September 9, 2020 from https://www.government-fleet.com/156182/policies-programs-to-reduce-distracted-driving
Google Scholar
Schoenau-Fog, H. (2011). The player engagement process—An exploration of continuation desire in digital games. Proceedings of DiGRA 2011 Conference: Think Design Play.
Google Scholar
Smith, M., & Gevins, A. (2005). Neurophysiologic monitoring of mental workload and fatigue during operation of a flight simulator. Proceedings of SPIE -The International Society for Optical Engineering (5797). Society of Photo-Optical Instrumentation Engineers. https://doi.org/10.1117/12.602181
Google Scholar
Statista. (2017). Video games advertising spending worldwide from 2010 to 2020 (in billion U.S. dollars). https://www.statista.com/statistics/238140/global-video-games-advertising-revenue/
Google Scholar
Sweetser, P., & Wyeth, P. (2005). GameFlow: A Model for Evaluating Player Enjoyment in Games. Computers in Entertainment, 3(3), 3–3. https://doi.org/10.1145/1077246.1077253
Google Scholar
Tamborini, R., &Skalski, P. (2006). The role of presence in the experience of electronic games. Playing video games: Motives, responses, and consequences(pp.225–240). Lawrence Erlbaum Associates Publishers.
Google Scholar
The motion monkey. (2017). UK Advergame Design & Development. https://www.themotionmonkey.co.uk/advergames/
Google Scholar
Tsipouras, M. G. (2019). Spectral information of EEG signals with respect to epilepsy classification. EURASIP Journal on Advances in Signal Processing, 2019(1), 10. https://doi.org/10.1186/s13634-019-0606-8
Google Scholar
United Nations World Food Programme. (2017). Food Force. https://web.archive.org/web/20050605073447/http://www.food-force.com/Unity—Analytics. (b.d.).
Google Scholar
Unity. Retrieved April 5, 2020 fromhttps://unity3d.com/unity/features/analytics
Google Scholar
Vourvopoulos, A., Bermudez,I.,Badia, S., & Liarokapis, F. (2017). EEG Correlates of Video Game Experience and User Profile in Motor-imagery-based Brain—Computer Interaction. The Visual Computer, 33(4), 533–546. https://doi.org/10.1007/s00371-016-1304-2
Google Scholar
Wachowiak, M., Smolikova-Wachowiak, R., Johnson, M., Hay, D., Power, K., & Williams-Bell, F. (2018). Quantitative feature analysis of continuous analytic wavelet transforms of electrocardiography and electromyography. Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences, 376, 20170250. https://doi.org/10.1098/rsta.2017.0250
Google Scholar
Yamada, F. (1998). Frontal midline theta rhythm and eyeblinking activity during a VDT task and a video game: Useful tools for psychophysiologyin ergonomics. Ergonomics, 41(5), 678–688. https://doi.org/10.1080/001401398186847
Google Scholar
Yang, M., Ewoldsen, D., Dinu, L., & Arpan, L. (2006). The Effectiveness of „in-Game” Advertising: Comparing College Students’ Explicit and Implicit Memory for Brand Names. Journal of Advertising, 35, 143–152. https://doi.org/10.2753/JOA0091-3367350410
Google Scholar
Yee, N. (2006). Motivations for play in online games. Cyberpsychology & Behavior : The Impact of the Internet, Multimedia and Virtual Reality on Behavior and Society, 9(6), 772–775. https://doi.org/10.1089/cpb.2006.9.772
Google Scholar
Yenigun, S. (2012). Presidential Campaigns Rock The Gamer Vote: NPR. https://www.npr.org/2012/10/01/162103528/presidential-campaigns-rock-the-gamer-vote?t=1582567815999
Google Scholar
Authors
Konrad BIERCEWICZkonrad.biercewicz@usz.edu.pl
Finance and Management Faculty of Economics, University of Szczecin, Szczecin Poland
Authors
Mariusz BORAWSKIFinance and Management Faculty of Economics, University of Szczecin, Szczecin Poland
Authors
Anna BORAWSKAFinance and Management Faculty of Economics, University of Szczecin, Szczecin Poland
Authors
Jarosław DUDAFinance and Management Faculty of Economics, University of Szczecin, Szczecin Poland
Statistics
Abstract views: 199PDF downloads: 143
License
All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.
Similar Articles
- Katarzyna KUREK, Maria SKUBLEWSKA-PASZKOWSKA, Mariusz DZIEŃKOWSKI, Paweł POWROŹNIK, THE IMPACT OF APPLYING UNIVERSAL DESIGN PRINCIPLES ON THE USABILITY OF ONLINE ACCOMMODATION BOOKING WEBSITES , Applied Computer Science: Vol. 20 No. 1 (2024)
- Islam MOHAMED, Mohamed EL-WAKAD, Khaled ABBAS, Mohamed ABOAMER, Nader A. Rahman MOHAMED, PUPIL DIAMETER AND MACHINE LEARNING FOR DEPRESSION DETECTION: A COMPARATIVE STUDY WITH DEEP LEARNING MODELS , Applied Computer Science: Vol. 20 No. 4 (2024)
- Błażej BADZIO, Agnieszka BODZIAK, Bartłomiej BRODAWKA, Karol BUCHAJCZUK, Maria SKUBLEWSKA-PASZKOWSKA, Mariusz DZIEŃKOWSKI, Paweł POWROŹNIK, ANALYSIS OF THE USABILITY AND ACCESSIBILITY OF WEBSITES IN VIEW OF THEIR UNIVERSAL DESIGN PRINCIPLES , Applied Computer Science: Vol. 18 No. 3 (2022)
- Amina ALYAMANI, Oleh YASNIY, CLASSIFICATION OF EEG SIGNAL BY METHODS OF MACHINE LEARNING , Applied Computer Science: Vol. 16 No. 4 (2020)
- Boutkhil SIDAOUI, PREDICTING STATES OF EPILEPSY PATIENTS USING DEEP LEARNING MODELS , Applied Computer Science: Vol. 20 No. 2 (2024)
- Ghania ZIDANI, Djalal DJARAH, Abdslam BENMAKHLOUF, Laid KHETTACHE, OPTIMIZING PEDESTRIAN TRACKING FOR ROBUST PERCEPTION WITH YOLOv8 AND DEEPSORT , Applied Computer Science: Vol. 20 No. 1 (2024)
- Denis RATOV, Vladimir LYFAR, MODELING TRANSMISSION MECHANISMS WITH DETERMINATION OF EFFICIENCY , Applied Computer Science: Vol. 16 No. 1 (2020)
- Zaid ALSAYGH, Zohair AL-AMEEN, CONTRAST ENHANCEMENT OF SCANNING ELECTRON MICROSCOPY IMAGES USING A NONCOMPLEX MULTIPHASE ALGORITHM , Applied Computer Science: Vol. 18 No. 2 (2022)
- Md. Torikur RAHMAN, A NOVEL APPROACH TO ENHANCE THE PERFORMANCE OF MOBILE AD HOC NETWORK (MANET) THROUGH A NEW BANDWIDTH OPTIMIZATION TECHNIQUE , Applied Computer Science: Vol. 15 No. 2 (2019)
- Marcin PERY, Robert WASZKOWSKI, COMPUTATIONAL SYSTEM FOR EVALUATING HUMAN PERCEPTION IN VIDEO STEGANOGRAPHY , Applied Computer Science: Vol. 20 No. 4 (2024)
You may also start an advanced similarity search for this article.
