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 wi...

Full description

Saved in:
Bibliographic Details
Published inInternational Conference on Affective Computing and Intelligent Interaction and workshops pp. 194 - 202
Main Authors Pinitas, Kosmas, Rasajski, Nemanja, Barthet, Matthew, Kaselimi, Maria, Makantasis, Konstantinos, Liapis, Antonios, Yannakakis, Georgios N.
Format Conference Proceeding
LanguageEnglish
Published IEEE 15.09.2024
Subjects
affect modelling
Affective computing
Complexity theory
Computational modeling
Context modeling
domain generalisation
engagement
FPS games
Games
Predictive models
Pressing
Transformers
Video games
Visualization
Online AccessGet full text

Cover

More Information
Summary: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.
ISSN:2156-8111
DOI:10.1109/ACII63134.2024.00027