Towards General Models of Player Experience: A Study Within Genres

To which degree can abstract gameplay metrics capture the player experience in a general fashion within a game genre? In this comprehensive study we address this question across three different videogame genres: racing, shooter, and platformer games. Using high-level gameplay features that feed preference learning models we are able to predict arousal accurately across different games of the same genre in a large-scale dataset of over 1, 000 arousal-annotated play sessions. Our genre models predict changes in arousal with up to 74% accuracy on average across all genres and 86% in the best cases. We also examine the feature importance during the modelling process and find that time-related features largely contribute to the performance of both game and genre models. The prominence of these game-agnostic features show the importance of the temporal dynamics of the play experience in modelling, but also highlight some of the challenges for the future of general affect modelling in games and beyond.

Automated summary

The study explores the ability of abstract gameplay metrics to predict arousal across different game genres, showing high accuracy in prediction. It highlights the significant contribution of time-related features to model performance, emphasizing the importance of temporal dynamics in gameplay experience modeling.