# Affective Game Computing: A Survey

arXiv: https://arxiv.org/abs/2309.14104 · HTML: https://mlsanity.com/paper/2309.14104/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 44 of its 266 references that are in the map.
- First posted on arXiv 25 Sep 2023.

## Builds on (most cited first, 30 of 44)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild](https://mlsanity.com/paper/1708.03985/) (2017)
- [Keystroke Dynamics as Part of Lifelogging](https://mlsanity.com/paper/2010.10144/) (2020)
- [First return, then explore](https://mlsanity.com/paper/2004.12919/) (2020)
- [Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey](https://mlsanity.com/paper/2006.11371/) (2020)
- [Real-time Deep Dynamic Characters](https://mlsanity.com/paper/2105.01794/) (2021)
- [Deep Learning for Procedural Content Generation](https://mlsanity.com/paper/2010.04548/) (2020)
- [SEWA DB: A Rich Database for Audio-Visual Emotion and Sentiment Research in the Wild](https://mlsanity.com/paper/1901.02839/) (2019)
- [Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognition](https://mlsanity.com/paper/2109.09559/) (2021)
- [Obstacle Tower: A Generalization Challenge in Vision, Control, and Planning](https://mlsanity.com/paper/1902.01378/) (2019)
- [Experience-Driven PCG via Reinforcement Learning: A Super Mario Bros Study](https://mlsanity.com/paper/2106.15877/) (2021)
- [Building Ethics into Artificial Intelligence](https://mlsanity.com/paper/1812.02953/) (2018)
- [The Arousal video Game AnnotatIoN (AGAIN) Dataset](https://mlsanity.com/paper/2104.02643/) (2021)
- [The Brussels Effect and Artificial Intelligence: How EU regulation will impact the global AI market](https://mlsanity.com/paper/2208.12645/) (2022)
- [The Pixels and Sounds of Emotion: General-Purpose Representations of Arousal in Games](https://mlsanity.com/paper/2101.10706/) (2021)
- [From Pixels to Affect: A Study on Games and Player Experience](https://mlsanity.com/paper/1907.02288/) (2019)
- [PAGAN: Video Affect Annotation Made Easy](https://mlsanity.com/paper/1907.01008/) (2019)
- [Contrastive Learning of Generalized Game Representations](https://mlsanity.com/paper/2106.10060/) (2021)
- [Towards General Models of Player Experience: A Study Within Genres](https://mlsanity.com/paper/2110.00978/) (2021)
- [Virtual Reality for Emotion Elicitation -- A Review](https://mlsanity.com/paper/2111.04461/) (2021)
- [AffectGAN: Affect-Based Generative Art Driven by Semantics](https://mlsanity.com/paper/2109.14845/) (2021)
- [Adaptive Music Composition for Games](https://mlsanity.com/paper/1907.01154/) (2019)
- [Privileged Information for Modeling Affect In The Wild](https://mlsanity.com/paper/2107.10552/) (2021)
- [TropeTwist: Trope-based Narrative Structure Generation](https://mlsanity.com/paper/2204.09672/) (2022)
- [Supervised Contrastive Learning for Affect Modelling](https://mlsanity.com/paper/2208.12238/) (2022)
- [Your Gameplay Says It All: Modelling Motivation in Tom Clancy's The Division](https://mlsanity.com/paper/1902.00040/) (2019)
- [Ethically Aligned Design of Autonomous Systems: Industry viewpoint and an empirical study](https://mlsanity.com/paper/1906.07946/) (2019)

## Built on by (most cited first, 11 of 11)

- [Large Language Models and Games: A Survey and Roadmap](https://mlsanity.com/paper/2402.18659/) (2024)
- [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)
- [Across-Game Engagement Modelling via Few-Shot Learning](https://mlsanity.com/paper/2409.13002/) (2024)
- [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)
- [Expanding Horizons of Level Diversity via Multi-objective Evolutionary Learning](https://mlsanity.com/paper/2509.24341/) (2025)
- [Resource-Constrained Affect Modelling via Variance Regularisation Pruning](https://mlsanity.com/paper/2605.27479/) (2026)
- [Spook the Machine: Gamified Exploration of Human Imagination of Machine Fear](https://mlsanity.com/paper/2609.15472/) (2026)

## Abstract (arXiv)

This paper surveys the current state of the art in affective computing principles, methods and tools as applied to games. We review this emerging field, namely affective game computing, through the lens of the four core phases of the affective loop: game affect elicitation, game affect sensing, game affect detection and game affect adaptation. In addition, we provide a taxonomy of terms, methods and approaches used across the four phases of the affective game loop and situate the field within this taxonomy. We continue with a comprehensive review of available affect data collection methods with regards to gaming interfaces, sensors, annotation protocols, and available corpora. The paper concludes with a discussion on the current limitations of affective game computing and our vision for the most promising future research directions in the field.