ACognitive Approach to Game Usability and Design: Mental Model Development in Novice Real-Time Strategy Gamers

Abstract

We developed a technique to observe and characterize a novice real-time-strategy (RTS) player's mental model as it shifts with experience. We then tested this technique using an off-the-shelf RTS game, EA Games Generals. Norman defined mental models as, "an internal representation of a target system that provides predictive and explanatory power to the operator." In the case of RTS games, the operator is the player and the target system is expressed by the relationships within the game. We studied five novice participants in laboratory-controlled conditions playing a RTS game. They played Command and Conquer Generals for 2 h per day over the course of 5 days. A mental model analysis was generated using player dissimilarity-ratings of the game's artificial intelligence (AI) agents analyzed using multidimensional scaling (MDS) statistical methods. We hypothesized that novices would begin with an impoverished model based on the visible physical characteristics of the game system. As they gained experience and insight, their mental models would shift and accommodate the functional characteristics of the AI agents. We found that all five of the novice participants began with the predicted physical-based mental model. However, while their models did qualitatively shift with experience, they did not necessarily change to the predicted functional-based model. This research presents an opportunity for the design of games that are guided by shifts in a player's mental model as opposed to the typical progression through successive performance levels.