# The Arousal video Game AnnotatIoN (AGAIN) Dataset

arXiv: https://arxiv.org/abs/2104.02643 · HTML: https://mlsanity.com/paper/2104.02643/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2021.

## Built on by (most cited first, 18 of 18)

- [Large Language Models and Games: A Survey and Roadmap](https://mlsanity.com/paper/2402.18659/) (2024)
- [Affective Game Computing: A Survey](https://mlsanity.com/paper/2309.14104/) (2023)
- [Supervised Contrastive Learning for Affect Modelling](https://mlsanity.com/paper/2208.12238/) (2022)
- [Generative Personas That Behave and Experience Like Humans](https://mlsanity.com/paper/2209.00459/) (2022)
- [Play with Emotion: Affect-Driven Reinforcement Learning](https://mlsanity.com/paper/2208.12622/) (2022)
- [The Invariant Ground Truth of Affect](https://mlsanity.com/paper/2210.07630/) (2022)
- [Measuring Diversity of Game Scenarios](https://mlsanity.com/paper/2404.15192/) (2024)
- [GameVibe: A Multimodal Affective Game Corpus](https://mlsanity.com/paper/2407.12787/) (2024)
- [Towards General Game Representations: Decomposing Games Pixels into Content and Style](https://mlsanity.com/paper/2307.11141/) (2023)
- [Can Large Language Models Capture Video Game Engagement?](https://mlsanity.com/paper/2502.04379/) (2025)
- [AI for Games in the Foundation Model Era](https://mlsanity.com/paper/2609.16679/) (2026)
- [Affectively Framework: Towards Human-like Affect-Based Agents](https://mlsanity.com/paper/2407.18316/) (2024)
- [Playing With Neuroscience: Past, Present and Future of Neuroimaging and Games](https://mlsanity.com/paper/2403.15413/) (2024)
- [Closing the Affective Loop via Experience-Driven Reinforcement Learning Designers](https://mlsanity.com/paper/2408.06346/) (2024)
- [The ProfessionAl Go annotation datasEt (PAGE)](https://mlsanity.com/paper/2211.01559/) (2022)
- [REVELIO -- Universal Multimodal Task Load Estimation for Cross-Domain Generalization](https://mlsanity.com/paper/2509.01642/) (2025)
- [PREFAB: PREFerence-based Affective Modeling for Low-Budget Self-Annotation](https://mlsanity.com/paper/2601.13904/) (2026)
- [Resource-Constrained Affect Modelling via Variance Regularisation Pruning](https://mlsanity.com/paper/2605.27479/) (2026)

## Abstract (arXiv)

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.