Not Every Moment is a Boss Fight — Affordance-Level Modeling of Attentional Structure in Games – CHI PLAY 2026

CHI PLAY 2026 · YorkWork in Progress

Publication Announcement: CHI PLAY 2026, York, UK

Institute of Psychology, RWTH Aachen University
Accepted · poster available now · final paper forthcoming

The first part of my PhD project has been accepted as a Work-in-Progress paper at CHI PLAY 2026.

The work introduces affordance-level attentional structure as a unit of analysis for player experience research. Instead of reading emotion off a physiological signal, it asks how a game’s recurring, designer-controlled affordances — interaction, combat, reward, pacing, decision-making — jointly shape a player’s overall arousal. Using electrodermal activity recorded across two structurally contrasting commercial games, it treats ridge regression as an interpretable lens rather than a predictive tool, and finds that arousal-linked attention tracks the demand to act under constraint more than the mere presence of narrative or spectacle.

August 2026 Updates

  • Accepted at CHI PLAY 2026
    Work-in-Progress track, York, UK
  • Poster & references available now
    Everything below is live — this is where the poster QR code leads
  • Final paper & ACM DOI — forthcoming
    This page will be updated with the official DOI and a link to the published article as soon as the proceedings are online

Access

How to cite

Work in Progress · citation
Rein, N., & Günther, T. (2026). Not Every Moment is a Boss Fight —
Affordance-Level Modeling of Attentional Structure in Games via
Physiological Arousal. Companion Proc. CHI PLAY 2026.
ACM. DOI: 10.1145/3800965.3834281 (forthcoming)

References

The five references shown on the poster are marked; the full list below supports the work.

  1. Aslam, H., & Brown, J. A. (2022). Affordance Theory in Game Design. Springer.
  2. Bansal, G., et al. (2021). Does the whole exceed its parts? The effect of AI explanations on complementary team performance. CHI 2021. ACM.
  3. Boucsein, W. (2012). Electrodermal Activity (2nd ed.). Springer.
  4. Braithwaite, J. J., Watson, D. G., Jones, R., & Rowe, M. (2013). A Guide for Analysing Electrodermal Activity (EDA) and Skin Conductance Responses (SCRs). University of Birmingham.
  5. Cacioppo, J. T., Tassinary, L. G., & Berntson, G. G. (Eds.). (2007). Handbook of Psychophysiology (3rd ed.). Cambridge University Press.
  6. Cardona-Rivera, R. E., & Young, R. M. (2014). A cognitivist theory of affordances for games. DiGRA 2013.
  7. Corbetta, M., & Shulman, G. L. (2002). Control of goal-directed and stimulus-driven attention in the brain. Nature Reviews Neuroscience, 3(3), 201–215.
  8. Dawson, M. E., Schell, A. M., & Filion, D. L. (2007). The electrodermal system. In Handbook of Psychophysiology (3rd ed.). Cambridge University Press.
  9. Drachen, A., Nacke, L. E., Yannakakis, G., & Pedersen, A. L. (2010). Correlation between heart rate, electrodermal activity and player experience in first-person shooter games. ACM SIGGRAPH Symposium on Video Games, 49–54.
  10. Fairclough, S. H. (2009). Fundamentals of physiological computing. Interacting with Computers, 21, 133–145.
  11. Green, C. S., & Bavelier, D. (2006). Effect of action video games on the spatial distribution of visuospatial attention. JEP: Human Perception and Performance, 32(6), 1465–1478.
  12. Hadiji, F., et al. (2014). Predicting player churn in the wild. IEEE CIG 2014, 1–8.
  13. Hodent, C. (2020). The Psychology of Video Games. CRC Press.
  14. Hougaard, B. I., & Knoche, H. (2024). Aiming, pointing, steering: A core task analysis framework for gameplay. PACM HCI, 8(CHI PLAY).
  15. Hunicke, R., LeBlanc, M., & Zubek, R. (2004). MDA: A formal approach to game design and game research. AAAI Workshop on Challenges in Game AI.
  16. Kahneman, D. (1973). Attention and Effort. Prentice Hall.
  17. Kivikangas, J. M., et al. (2011). A review of the use of psychophysiological methods in game research. Journal of Gaming & Virtual Worlds, 3(3), 181–199.
  18. Lang, A. (1995). The limited capacity model of mediated message processing. Journal of Communication, 45(3), 46–70.
  19. Lykken, D. T., Rose, R., Luther, B., & Maley, M. (1966). Correcting psychophysiological measures for individual differences in range. Psychological Bulletin, 66(6), 481–484.
  20. Mandryk, R. L., & Atkins, M. S. (2007). A fuzzy physiological approach for continuously modeling emotion during interaction with play technologies. IJHCS, 65(4), 329–347.
  21. Mirza-Babaei, P., Nacke, L. E., Gregory, J., Collins, N., & Fitzpatrick, G. (2013). How does it play better? Exploring user testing and biometric storyboards in games user research. CHI 2013, 1499–1508.
  22. Nacke, L. E., Kalyn, M., Lough, C., & Mandryk, R. L. (2011). Biofeedback game design: Using direct and indirect physiological control to enhance game interaction. CHI 2011, 103–112.
  23. Petersen, S. E., & Posner, M. I. (2012). The attention system of the human brain: 20 years after. Annual Review of Neuroscience, 35, 73–89.
  24. Rudin, C., et al. (2022). Interpretable machine learning: Fundamental principles and 10 grand challenges. Statistics Surveys, 16, 1–85.

Natali Rein · Institute of Psychology, RWTH Aachen University · contact

Part of an ongoing PhD project on physiological modeling of player experience for user-centric game design.

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