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

## TL;DR

This study demonstrates that deep convolutional neural networks can predict player arousal levels from gameplay videos with high accuracy, advancing affective computing in gaming and beyond.

## Contribution

It introduces a novel, general approach using deep learning to predict affective states from video streams, applicable across various domains.

## Key findings

- Achieved over 78% average accuracy in classifying arousal levels.
- Revealed that deep models can predict affect with high reliability.
- Validated the approach on a dataset of 50 gameplay videos.

## Abstract

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.