Disruptive game design
: a commercial design and development methodology for supporting player cognitive engagement in digital games

Student thesis: Doctoral Thesis


First-person games often support the player’s gradual accretion of knowledge of the game’s rules during gameplay. They thus focus on challenging and developing performative skills, which in turn supports the player in attaining feelings of achievement and skills mastery. However, an alternative disruptive game design approach is proposed as an approach that encourages players to engage in higher-order thinking, in addition to performative challenges. This requires players to cognitively engage with the game at a deeper level. This stems from the player’s expectations of game rules and behaviours being disrupted, rather than supported, requiring players to learn and re-learn the game rules as they play. This disruptive approach to design aims to support players in satiating their needs for not only achievement and mastery at a performative level but also, their needs for problem-solving and creativity.
Utilising a Research through Design methodology, a model of game space proposes different stages of a game’s creation, from conceptualisation through to the final player experience. The Ludic Action Model (LAM), developed from existing game studies and cognitive psychological theory, affords an understanding of how the player forms expectations in the game as played. A conceptual framework of game components is then constructed and mapped to the Ludic Action Model, providing a basis for understanding how different components of a game interact with and influence the player’s cognitive and motor processes. The Ludic Action Model and the conceptual framework of game components are used to construct the Disruptive Game Feature Design and Development (DisDev) model, created as a design tool for ‘disruptive’ games. The disruptive game design approach is then applied to the design, development, and publication of a commercial game, Amnesia: A Machine for Pigs (The Chinese Room, 2013). This application demonstrated the suitability of the design approach, and the proposed models, for establishing disruptive game features in the game as designed, developing those features in the game as created, to the final resolution in the game as published, which the player will then experience in the game as played.
A phenomenological template analysis of online player discussions of the game shows that players tend to evaluate their personal game as played (i.e. their personal play experience) in relation to their a priori game as expected (i.e. the experience that they expected the game to provide). Players reported their play experiences in ways that suggested they had experienced cognitive engagement and higher-order thinking. However, player attitudes towards this type of play experience were highly polarised and seemingly dependent on the correspondence between actual and expected play experiences. The discussion also showed that different methods of disruption have a variable effect on the player experience depending on the primacy of the game feature being disrupted. Primary features are more effectively disrupted when the game’s responses to established player actions are subsequently altered. Secondary game features, only present in some sections, are most effectively disrupted when their initially contextualised behaviour is subsequently altered, or recontextualised. In addition, story-based feature disruption is most effected when the initial encoding stage is ambiguous, thus disrupting players’ attempts to form an initial understanding of them. However, these different methods of disruption may be most effective when used in conjunction with each other.
Date of AwardNov 2015
Original languageEnglish
SupervisorBrett Stevens (Supervisor), Mark Eyles (Supervisor) & Daniel Mcguire Pinchbeck (Supervisor)


  • disruptive game design
  • game design
  • games
  • game psychology
  • player psychology
  • cognitive psychology
  • game development
  • amnesia
  • amnesia: a machine for pigs
  • machine for pigs
  • AMFP
  • ludic action model
  • ludic cognition model
  • ludic knowledge
  • player expectation

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