TY - JOUR
T1 - Mastering uncertainty
T2 - A predictive processing account of enjoying uncertain success in video game play
AU - Deterding, Sebastian
AU - Andersen, Marc
AU - Kiverstein, Julian
AU - Miller, Mark
PY - 2022/7/26
Y1 - 2022/7/26
N2 - Why do we seek out and enjoy uncertain success in playing games? Game designers and researchers suggest that games whose challenges match player skills afford engaging experiences of achievement, competence, or effectance—of doing well. Yet, current models struggle to explain why such balanced challenges best afford these experiences and do not straightforwardly account for the appeal of high- and low-challenge game genres like Idle and Soulslike games. In this article, we show that Predictive Processing (PP) provides a coherent formal cognitive framework which can explain the fun in tackling game challenges with uncertain success as the dynamic process of reducing uncertainty surprisingly efficiently. In gameplay as elsewhere, people enjoy doing better than expected, which can track learning progress. In different forms, balanced, Idle, and Soulslike games alike afford regular accelerations of uncertainty reduction. We argue that this model also aligns with a popular practitioner model, Raph Koster’s Theory of Fun for Game Design, and can unify currently differentially modelled gameplay motives around competence and curiosity.
AB - Why do we seek out and enjoy uncertain success in playing games? Game designers and researchers suggest that games whose challenges match player skills afford engaging experiences of achievement, competence, or effectance—of doing well. Yet, current models struggle to explain why such balanced challenges best afford these experiences and do not straightforwardly account for the appeal of high- and low-challenge game genres like Idle and Soulslike games. In this article, we show that Predictive Processing (PP) provides a coherent formal cognitive framework which can explain the fun in tackling game challenges with uncertain success as the dynamic process of reducing uncertainty surprisingly efficiently. In gameplay as elsewhere, people enjoy doing better than expected, which can track learning progress. In different forms, balanced, Idle, and Soulslike games alike afford regular accelerations of uncertainty reduction. We argue that this model also aligns with a popular practitioner model, Raph Koster’s Theory of Fun for Game Design, and can unify currently differentially modelled gameplay motives around competence and curiosity.
KW - active inference
KW - competence
KW - game enjoyment
KW - gaming motivation
KW - predictive processing
KW - uncertainty
KW - video games
UR - http://www.scopus.com/inward/record.url?scp=85135620963&partnerID=8YFLogxK
U2 - 10.3389/fpsyg.2022.924953
DO - 10.3389/fpsyg.2022.924953
M3 - Tidsskriftartikel
C2 - 35959012
SN - 1664-1078
VL - 13
JO - Frontiers in Psychology
JF - Frontiers in Psychology
M1 - 924953
ER -