Affiliation:
1. Imperial College, London, UK
2. University of York, York, UK
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.
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Networks and Communications,Human-Computer Interaction,Social Sciences (miscellaneous)
Cited by
3 articles.
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