In networked virtual reality (VR), user behaviors, individual differences, and group dynamics can serve as important signals for future speech behaviors, such as who the next speaker will be and the timing of turn-taking behaviors. The ability to predict and understand these behaviors offers opportunities to provide adaptive and personalized assistance, for example helping users with varying sensory abilities navigate complex social scenes and instantiating virtual moderators with natural behaviors. In this work, we predict turn-taking behaviors using features extracted based on social dynamics literature. We discuss results from a large-scale VR classroom dataset consisting of 77 sessions and 1660 minutes of small-group social interactions collected over four weeks. In our evaluation, gradient boosting classifiers achieved the best performance, with accuracies of 0.71-0.78 AUC (area under the ROC curve) across three tasks concerning the ''what'', ''who'', and ''when'' of turn-taking behaviors. In interpreting these models, we found that group size, listener personality, speech-related behavior (e.g., time elapsed since the listener's last speech event), group visual attention (e.g., the group's head orientation towards the speaker), and the listener and previous speaker's head pitch, head y-axis position, and left hand y-axis position more saliently influenced predictions. Results suggested that these features remain reliable indicators in novel social VR settings, as prediction performance is robust over time and with groups and activities not used in the training dataset. We discuss theoretical and practical implications of the work.

Social Virtual Reality (VR) typically entails users interacting in real time. However, asynchronous Social VR presents the possibility of combining the convenience of asynchronous communication with the high presence of VR. Because the tools to easily record and replay VR social interactions are fairly new, scholars have not yet examined how users perceive asynchronous VR social interactions, and how nonverbal transformations of recorded interactions influence user behavior. In this work, we study nonverbal transformations of group interactions around proxemics and gaze and present results from an exploratory user study (N=128) investigating their effects. We found that the combination of spatial accommodation and added gaze increases social presence, perceived attention, and mutual gaze. Results also showed an inverse relationship between interpersonal distance and perceived levels of dominance and threat of the recorded group. Finally, we outline implications for educators and virtual meeting organizers to incorporate these transformations into real-world scenarios.

We present a prototype virtual reality user interface for robot teleoperation that supports high-level specification of 3D object positions and orientations in remote assembly tasks. Users interact with virtual replicas of task objects. They asynchronously assign multiple goals in the form of 6DoF destination poses without needing to be familiar with specific robots and their capabilities, and manage and monitor the execution of these goals. The user interface employs two different spatiotemporal visualizations for assigned goals: one represents all goals within the user’s workspace (Aggregated View), while the other depicts each goal within a separate world in miniature (Timeline View). We conducted a user study of the interface without the robot system to compare how these visualizations affect user efficiency and task load. The results show that while the Aggregated View helped the participants finish the task faster, the participants preferred the Timeline View.

Work on cueing performance in AR and VR has focused on sequential tasks in which each step must be completed in order before the user can proceed to the next. However, for unordered tasks such as putting books back on a library shelf, the user may be able to perform multiple steps concurrently without needing to follow a specific order. In such situations, giving the user multiple cues for potentially concurrent steps may improve performance time. To investigate this, we built a bimanual VR testbed in which the user needs to move objects to designated destinations, guided by different numbers of cues. The user can decide the order to perform the cued steps and, in some conditions, can affect which cues are shown.In a formal user study, we found that in most conditions, participants perform fastest with three cues. Dynamically updating the set of displayed cues based on hand proximity improves performance, and updating the set based on eye gaze improves performance even more. Finally, for both the hand-proximity and eye-gaze mechanisms, performance can be further improved by locking the cues for objects predicted to be moved next based on hand distance.