Deterding Sebastian, Andersen Marc Malmdorf, Kiverstein Julian, Miller Mark
Dyson School of Design Engineering, Imperial College London, London, United Kingdom.
Digital Creativity Labs, University of York, York, United Kingdom.
Front Psychol. 2022 Jul 26;13:924953. doi: 10.3389/fpsyg.2022.924953. eCollection 2022.
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 . 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 , 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 , and can unify currently differentially modelled gameplay motives around competence and curiosity.
为什么我们在玩游戏时会寻求并享受不确定的成功？游戏设计师和研究人员认为，挑战与玩家技能相匹配的游戏能带来关于成就、能力或效能感的引人入胜的体验。然而，当前的模型难以解释为什么这种平衡的挑战最能带来这些体验，也不能直接解释像放置类游戏和魂类游戏等高挑战和低挑战游戏类型的吸引力。在本文中，我们表明预测处理（PP）提供了一个连贯的形式认知框架，该框架可以将以不确定的成功应对游戏挑战中的乐趣解释为令人惊讶地高效减少不确定性的动态过程。在游戏玩法中，与其他地方一样，人们享受能够跟踪学习进度的过程。以不同形式出现的平衡类、放置类和魂类游戏都能定期加速不确定性的减少。我们认为，这个模型也与一个流行的从业者模型——拉斐尔·科斯特的模型相一致，并且可以将目前围绕能力和好奇心进行差异化建模的游戏动机统一起来。
