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

Selective sustained attention (SSA) is an importantcognitive process that enables everyday functioning and taskperformance by allowing us to: 1) choose components of ourenvironment to process at the exclusion of others and 2)maintain focus on those components over time. AlthoughSSA is known to undergo rapid and marked changes duringthe preschool and early primary school children years, therehas been a paucity of behavioral data on these years ofdevelopment due to a lack of child-appropriate testingparadigms. TrackIt is a paradigm that was recently developedto fill the previously existing measurement gap for SSA inthese years. In this study, we analyzed errors that children(aged 3-7) make when performing TrackIt, to betterunderstand what factors drive improvement in theirperformance over age. In addition, we manipulatedparameters within TrackIt to place varying levels of demandon children’s SSA, and measured behavioral performanceover age, with the goal of measuring and characterizingdevelopmental trends during these years. Since TrackIt is stilla recent paradigm, our results also help suggest appropriateparameter settings for calibrating the task to different agegroups.