Varying the Context to Advance Affect Modelling: A Study on Game Engagement Prediction

Affective computing faces a pressing challenge: the limited ability of affect models to generalise amidst varying contextual factors within the same task. While well recognised, this challenge persists due to the absence of suitable large-scale corpora with rich and diverse contextual information within a domain. To address this challenge, this paper introduces a GameVibe, a novel corpus explicitly tailored to confront the lack of contextual diversity. The affect corpus is sourced from 30 First Person Shooter (FPS) games, showcasing diverse game modes and designs within the same domain. The corpus comprises 2 hours of annotated gameplay videos with engagement levels annotated by a total of 20 participants in a time-continuous manner. Our preliminary analysis on this corpus sheds light on the complexity of generalising affect predictions across contextual variations in similar affective computing tasks. These initial findings serve as a catalyst for further research, inspiring deeper inquiries into this critical, yet understudied, aspect of affect modelling.