Gesture interaction is standard in modern extended reality head-mounted displays, e.g., Microsoft HoloLens 2 and Apple Vision Pro. These gestures come from multiple sources, such as elicitation studies, the application designer, or an expert. However, it is unclear if the source of the gesture affects the memorability of gestures (i.e., level of recall). In a user study, 35 participants used the HoloLens 2 to test three gestures set from different sources: 1) expert-defined, whereby we asked four experts to define gestures; 2) user-defined, whereby each participant defined their gestures; and 3) elicitation-defined, whereby we used gestures proposed by previous work. Our results showed that expert-defined gestures (72.73% recall) were more likely to be remembered than elicitation-defined (59.87% recall) gestures. Further, user-defined gestures (88.75% recall) were the most memorable. These data suggest that the source of gestures has a substantial impact on their memorability.

Visually searching for objects is an everyday task. In many contexts, people must visually search for multiple objects at the same time while avoiding distractor objects, such as triage during a mass casualty incident. While many prior augmented reality (AR) and virtual reality (VR) studies have investigated cues to aid in visual search tasks, few have investigated cues in contexts involving multiple targets and distractors with a full 360° effective field of regard (EFOR). Individually, multiple targets, distractors, and a full 360° EFOR each add complexity to visual search; when combined, they compound the difficulty even further. In this paper, we present such a study that compares three common types of visual cues (2D Wedge, 3D Arrow, and Gaze Line) to a baseline condition with no cueing for a 360° visual search task. Our results reinforce the importance of providing some type of cue, with the Gaze Line design being particularly beneficial. We discuss the potential implications of these findings for designing cues specifically for such complex visual search tasks.

Despite knowing exactly what an object looks like, searching for it in a person's visual field is a time-consuming and error-prone experience. In Augmented Reality systems, new algorithms are proposed to speed up search time and reduce human errors. However, these algorithms might not always provide 100% accurate visual cues, which might affect users' perceived reliability of the algorithm and, thus, search performance. Here, we examined the detrimental effects of automation bias caused by imperfect cues presented in the Augmented Reality head-mounted display using the YOLOv5 machine learning model. 53 participants in the two groups received either 100% accurate visual cues or 88.9% accurate visual cues. Their performance was compared with the control condition, which did not include any additional cues. The results show how cueing may increase performance and shorten search times. The results also showed that performance with imperfect automation was much worse than perfect automation and that, consistent with automation bias, participants were frequently enticed by incorrect cues.