Objective Difficulty-Skill Balance Impacts Perceived Balance but Not Behaviour: A Test of Flow and Self-Determination Theory Predictions

Q1 Social Sciences
Sebastian Deterding, Joe Cutting
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Abstract

Flow and self-determination theory predict that game difficulty in balance with player skill maximises enjoyment and engagement, mediated by attentive absorption or competence. Yet recent evidence and methodological concerns are challenging this view, and key theoretical predictions have remained untested, importantly which objective difficulty-skill ratio is perceived as most balanced. To test these, we ran a preregistered study (n=309) using a Go-like 2-player game with an AI opponent, randomly assigning players to one of three objective difficulty-skill ratios (AI plays to win, draw, or lose) over five matches. The AI successfully manipulated objective balance, with the draw condition perceived as most balanced. However, balance did not impact play behaviour, nor did we find the predicted uniform 'inverted-U' between balance and positive play experiences. Importantly, we found both theories too underspecified to severely test. We conclude that balance and competence likely matter less for behavioural engagement than commonly held. We propose alternative factors such as player appraisals, novelty, and progress, and debate the value and challenges of theory-testing work in games HCI.
客观难度-技能平衡影响感知平衡,但不影响行为:心流理论和自我决定理论预测的检验
心流理论和自我决定理论预测,平衡游戏难度与玩家技能之间的关系,通过专注或能力调节,可以最大化乐趣和粘性。然而,最近的证据和方法论问题正在挑战这一观点,关键的理论预测仍然未经检验,重要的是,客观难度-技能比被认为是最平衡的。为了测试这些,我们进行了一项预先注册的研究(n=309),使用与AI对手的类似围棋的2人游戏,在五场比赛中随机分配玩家三种客观难度-技能比率(AI获胜,平局或失败)中的一种。AI成功地操纵了客观平衡,并将绘制条件视为最平衡的。然而,平衡并不会影响游戏行为,我们也没有发现平衡与积极游戏体验之间的“倒u”关系。重要的是,我们发现这两种理论都不够明确,无法进行严格的测试。我们的结论是,平衡和能力对行为参与的影响可能没有通常认为的那么大。我们提出了玩家评价、新颖性和进步等替代因素,并讨论了游戏HCI中理论测试工作的价值和挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Proceedings of the ACM on Human-Computer Interaction
Proceedings of the ACM on Human-Computer Interaction Social Sciences-Social Sciences (miscellaneous)
CiteScore
5.90
自引率
0.00%
发文量
257
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