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This article reports on an investigation of the use of convolutional neuralnetworks to predict the visual attention of chess players. The visual attentionmodel described in this article has been created to generate saliency maps thatcapture hierarchical and spatial features of chessboard, in order to predictthe probability fixation for individual pixels Using a skip-layer architectureof an autoencoder, with a unified decoder, we are able to use multiscalefeatures to predict saliency of part of the board at different scales, showingmultiple relations between pieces. We have used scan path and fixation datafrom players engaged in solving chess problems, to compute 6600 saliency mapsassociated to the corresponding chess piece configurations. This corpus iscompleted with synthetically generated data from actual games gathered from anonline chess platform. Experiments realized using both scan-paths from chessplayers and the CAT2000 saliency dataset of natural images, highlights severalresults. Deep features, pretrained on natural images, were found to be helpfulin training visual attention prediction for chess. The proposed neural networkarchitecture is able to generate meaningful saliency maps on unseen chessconfigurations with good scores on standard metrics. This work provides abaseline for future work on visual attention prediction in similar contexts.

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