'Institute of Electrical and Electronics Engineers (IEEE)'
Abstract
This project has received funding from the EU’s Horizon 2020 programme
under grant agreement No 951911, and from the University of Malta internal
research grants programme Research Excellence Fund under grant agreement
No 202003.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 largescale 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.peer-reviewe
