Sharing gaze rays for visual target identification tasks in collaborative augmented reality

Augmented reality (AR) technologies provide a shared platform for users to collaborate in a physical context involving both real and virtual content. To enhance the quality of interaction between AR users, researchers have proposed augmenting users’ interpersonal space with embodied cues such as their gaze direction. While beneficial in achieving improved interpersonal spatial communication, such shared gaze environments suffer from multiple types of errors related to eye tracking and networking, that can reduce objective performance and subjective experience. In this paper, we present a human-subjects study to understand the impact of accuracy, precision, latency, and dropout based errors on users’ performance when using shared gaze cues to identify a target among a crowd of people. We simulated varying amounts of errors and the target distances and measured participants’ objective performance through their response time and error rate, and their subjective experience and cognitive load through questionnaires. We found significant differences suggesting that the simulated error levels had stronger effects on participants’ performance than target distance with accuracy and latency having a high impact on participants’ error rate. We also observed that participants assessed their own performance as lower than it objectively was. We discuss implications for practical shared gaze applications and we present a multi-user prototype system.