Towards General Models of Player Experience: A Study Within Genres

TL;DR

This study investigates whether high-level gameplay metrics can predict player arousal across different game genres, demonstrating promising accuracy and emphasizing the importance of temporal features in modeling player experience.

Contribution

It introduces genre-specific models that predict arousal using abstract gameplay features, highlighting the role of temporal dynamics in general affect modeling.

Findings

Genre models predict arousal with up to 86% accuracy.

Time-related features are the most significant predictors.

Temporal dynamics are crucial for general player experience modeling.

Abstract

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.