A Hidden Markov Model for Analyzing Eye-Tracking of Moving Objects

Abstract

Eye-tracking provides an opportunity to generate and analyze high-density data relevant to understanding cognition. However, while objects in the real world are often dynamic, eyetracking paradigms are typically limited to assessing gaze toward static objects. In this study, we propose a generative framework, based on a hidden Markov model, for using eyetracking data to analyze behavior in the context of multiple moving objects of interest. We apply this framework to analyze 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 we use to tune and validate our model, while providing insights into the visual object tracking abilities of children and adults.