Eye Tracking Data

Eye-tracking data, recording eye movements to understand visual attention, is increasingly used across diverse fields to analyze human perception and cognition. Current research focuses on developing robust algorithms, including deep learning models like transformers, convolutional neural networks, and recurrent neural networks (e.g., LSTMs), to process this data for applications such as predicting saliency, classifying cognitive states (e.g., cognitive load, disease diagnosis), and improving human-computer interaction. This data offers valuable insights into human behavior, impacting fields from education and healthcare (e.g., diagnosing neurological disorders) to human-robot interaction and the design of user interfaces. The development of standardized data quality reporting and large-scale, publicly available datasets is also a significant area of ongoing effort.