Eye tracking in simple visual search tasks

Abstract

Eye tracking has become an essential technique for understanding human visual attention and behavior across a wide range of fields. One of the core challenges in this domain is ac curately predicting gaze during visual search tasks. As traditional models using handcrafted features often lack generalizability,Recent advances in deep learning offers a powerful alterna tives by enabling data-driven learning of complex spatial patterns in visual attention. This research introduces a deep learning-based eye-tracking system aimed at predicting vi sual attention through saliency maps, using the uEyes dataset which features a variety of image categories including desktop, mobile, web, and posters, along with corresponding human eye tracking data . the system employs a U-Net convolutional neural network optimized for pixel level saliency prediction. The model is trained and evaluated using a robust set of performance metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Kullback-Leibler Divergence (KLD), Correlation Coefficient (CC), Histogram Similarity (SIM), and Accuracy. This system showcases the effectiveness of deep learning in modeling human visual behav ior in visual search tasks.