The Arousal video Game AnnotatIoN (AGAIN) Dataset

Abstract

How can we model affect in a general fashion, across dissimilar tasks, and to which degree are such general representations of affect even possible? To address such questions and enable research towards general affective computing, this paper introduces The Arousal video Game AnnotatIoN (AGAIN) dataset. AGAIN is a large-scale affective corpus that features over 1,100 in-game videos (with corresponding gameplay data) from nine different games, which are annotated for arousal from 124 participants in a first-person continuous fashion. Even though AGAIN is created for the purpose of investigating the generality of affective computing across dissimilar tasks, affect modelling can be studied within each of its 9 specific interactive games. To the best of our knowledge AGAIN is the largest -- over 37 hours of annotated video and game logs -- and most diverse publicly available affective dataset based on games as interactive affect elicitors.

arXiv commentPublished in the IEEE Transactions on Affective Computing (2022). Available on IEEE Xplore: https://ieeexplore.ieee.org/document/9816018

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• Playing With Neuroscience: Past, Present and Future of Neuroimaging and Games
• The ProfessionAl Go annotation datasEt (PAGE)
• PREFAB: PREFerence-based Affective Modeling for Low-Budget Self-Annotation
• Emoception: Selective Affective Layer Fine-Tuning of Video Vision Transformers for Player Arousal Change Recognition From Gameplay Footage