EyeBench: Predictive Modeling from Eye Movements in Reading
Omer Shubi, David Robert Reich, Keren Klein, Yuval Angel, Paul Prasse, Lena A. Jäger, Yevgeni Berzak
We introduceEyeBench, a first-of-its-kind benchmark for evaluating multimodal machine learning models that decode cognitive and linguistic information from eye movements during reading. EyeBench provides an accessible entry point to the highly challenging and underexplored domain of eye movements paired with text, with the aim of driving innovation in multimodal AI and cognitive science. The benchmark provides a standardized framework for evaluating predictive models on a diverse set of datasets and tasks, ranging from prediction of reading comprehension to automatic assessment of a reader's experienced text difficulty. Progress on the EyeBench challenge will open the door for  both practical real-world applications, such as adaptive user interfaces and personalized education, and scientific advances in understanding human language processing.