Proceedings of the Annual Meeting of the Cognitive Science Society

Eye-tracking provides an opportunity to generate and analyzehigh-density data relevant to understanding cognition. How-ever, while objects in the real world are often dynamic, eye-tracking paradigms are typically limited to assessing gaze to-ward static objects. In this study, we propose a generativeframework, based on a hidden Markov model, for using eye-tracking data to analyze behavior in the context of multiplemoving objects of interest. We apply this framework to ana-lyze data from a recent visual object tracking task paradigm,TrackIt, for studying selective sustained attention in children.We also present a novel ‘supervised’ variant of TrackIt that weuse to tune and validate our model, while providing insightsinto the visual object tracking abilities of children and adults