Eugene Hwang, Jeongmi Lee

With the proliferation of Extended Reality (XR) devices, the integration of physiological sensors, particularly eye-trackers, has become commonplace and accessible. However, even with the abundance of data, the full potential that the gaze data can provide is yet to be discovered. This study focuses on how the gaze data can be used to determine the subtle, nuanced attention state of users and the application potential of the gaze components. This study proposes a novel methodology for identifying attentional lapses in an XR environment and the corresponding gaze components. This research contributes to the broader understanding of attention and defines the gaze components that can be used to predict the attentional fluctuation of users. Findings of this study lay the groundwork for more precise and effective monitoring of user attention in XR.