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

AbstractEye-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, eye-tracking 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 eye-tracking 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.

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