# From Pixels to Affect: A Study on Games and Player Experience

arXiv: https://arxiv.org/abs/1907.02288 · HTML: https://mlsanity.com/paper/1907.02288/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jul 2019.

## Built on by (most cited first, 14 of 14)

- [From the Lab to the Wild: Affect Modeling via Privileged Information](https://mlsanity.com/paper/2305.10919/) (2023)
- [Contrastive Learning of Generalized Game Representations](https://mlsanity.com/paper/2106.10060/) (2021)
- [Affective Game Computing: A Survey](https://mlsanity.com/paper/2309.14104/) (2023)
- [AffectGAN: Affect-Based Generative Art Driven by Semantics](https://mlsanity.com/paper/2109.14845/) (2021)
- [Supervised Contrastive Learning for Affect Modelling](https://mlsanity.com/paper/2208.12238/) (2022)
- [Go-Blend behavior and affect](https://mlsanity.com/paper/2109.13388/) (2021)
- [Learning Task-Independent Game State Representations from Unlabeled Images](https://mlsanity.com/paper/2206.06490/) (2022)
- [The Invariant Ground Truth of Affect](https://mlsanity.com/paper/2210.07630/) (2022)
- [RankNEAT: Outperforming Stochastic Gradient Search in Preference Learning Tasks](https://mlsanity.com/paper/2204.06901/) (2022)
- [Across-Game Engagement Modelling via Few-Shot Learning](https://mlsanity.com/paper/2409.13002/) (2024)
- [Towards General Game Representations: Decomposing Games Pixels into Content and Style](https://mlsanity.com/paper/2307.11141/) (2023)
- [AI for Games in the Foundation Model Era](https://mlsanity.com/paper/2609.16679/) (2026)
- [Modeling Player Personality Factors from In-Game Behavior and Affective Expression](https://mlsanity.com/paper/2308.14224/) (2023)
- [Future Research Avenues for Artificial Intelligence in Digital Gaming: An Exploratory Report](https://mlsanity.com/paper/2412.14085/) (2024)

## Abstract (arXiv)

Is it possible to predict the affect of a user just by observing her behavioral interaction through a video? How can we, for instance, predict a user's arousal in games by merely looking at the screen during play? In this paper we address these questions by employing three dissimilar deep convolutional neural network architectures in our attempt to learn the underlying mapping between video streams of gameplay and the player's arousal. We test the algorithms in an annotated dataset of 50 gameplay videos of a survival shooter game and evaluate the deep learned models' capacity to classify high vs low arousal levels. Our key findings with the demanding leave-one-video-out validation method reveal accuracies of over 78% on average and 98% at best. While this study focuses on games and player experience as a test domain, the findings and methodology are directly relevant to any affective computing area, introducing a general and user-agnostic approach for modeling affect.