# Deep learning investigation for chess player attention prediction using eye-tracking and game data

## TL;DR

This paper explores using convolutional neural networks with eye-tracking and game data to predict chess players' visual attention, generating saliency maps that reflect hierarchical and spatial features of the chessboard.

## Contribution

It introduces a novel CNN architecture with a skip-layer autoencoder to predict multi-scale saliency maps for chess, integrating natural image features and game data.

## Key findings

- Pretrained deep features improve attention prediction accuracy.
- The model generates meaningful saliency maps on unseen chess positions.
- Results show good performance on standard saliency metrics.

## Abstract

This article reports on an investigation of the use of convolutional neural networks to predict the visual attention of chess players. The visual attention model described in this article has been created to generate saliency maps that capture hierarchical and spatial features of chessboard, in order to predict the probability fixation for individual pixels Using a skip-layer architecture of an autoencoder, with a unified decoder, we are able to use multiscale features to predict saliency of part of the board at different scales, showing multiple relations between pieces. We have used scan path and fixation data from players engaged in solving chess problems, to compute 6600 saliency maps associated to the corresponding chess piece configurations. This corpus is completed with synthetically generated data from actual games gathered from an online chess platform. Experiments realized using both scan-paths from chess players and the CAT2000 saliency dataset of natural images, highlights several results. Deep features, pretrained on natural images, were found to be helpful in training visual attention prediction for chess. The proposed neural network architecture is able to generate meaningful saliency maps on unseen chess configurations with good scores on standard metrics. This work provides a baseline for future work on visual attention prediction in similar contexts.