Jeremy N. Bailenson’s research while affiliated with Stanford University and other places

p dir="ltr"> Virtual reality (VR) can evoke powerful emotions and transport people to diverse environments, making it a compelling tool for storytelling and learning. However, those who are anxious about technology may need to spend more cognitive resources and could have negative experiences because of the complexity of the medium, altering how they experience the narrative. Training may help people better-navigate virtual experiences by providing guidance. This study evaluates how individuals’ training type (paper, video, and VR) and computer anxiety influenced outcomes. We measured performance and self-reported outcomes while participants ( n = 284) navigated scenes, interacted with objects, and watched a narrative 360° video. Results showed that compared to participants who received training in paper, participants who received training in video or VR mastered more VR functions. Compared to participants who received training in video, those who trained directly in VR had less of a negative VR experience. Furthermore, participants who trained in VR perceived the training as more useful and found the VR tasks to be easier compared to those who received training in paper or video. Additionally, those with high levels of computer anxiety, regardless of training, had more negative outcomes than those with low computer anxiety, including less mastery of VR functions and engagement with the 360° video content, more of a negative VR experience, perceived the training as being less useful, and completed tasks with more difficulty. We discuss implications for theories of information processing in VR, as well as implications for scaled engagement with narratives and learning in VR. </p

How has the field of Virtual Reality (VR) evolved and what type of research has made an impact? We used natural language processing techniques and generative Artificial Intelligence to develop the most complete review of VR research to date (1984-2024). Among 196,734 paper abstracts, 13 reliable themes emerged over time, with immersive experiences receiving the most recent attention, and papers with statistically significant findings having the most impact. Interdisciplinary teams were cited more than less interdisciplinary teams, and watershed moments like mainstream industry embracing VR (i.e., Google Cardboard’s release) modified scholars’ research focus. Research topics were heterogeneous across authors’ regions of the world and academic fields. Papers in communication science had more interdisciplinary teams and focused more on immersive experiences than papers in other social scientific and non-social scientific fields. Our database — the VRbalARchive — is publicly available, helping scholars investigate VR’s history and enhancing our theoretical understanding of the medium.

Social virtual reality is an emerging medium of communication. In this medium, a user's avatar (virtual representation) is controlled by the tracked motion of the user's headset and hand controllers. This tracked motion is a rich data stream that can leak characteristics of the user or can be effectively matched to previously-identified data to identify a user. To better understand the boundaries of motion data identifiability, we investigate how varying training data duration and train-test delay affects the accuracy at which a machine learning model can correctly classify user motion in a supervised learning task simulating re-identification. The dataset we use has a unique combination of a large number of participants, long duration per session, large number of sessions, and a long time span over which sessions were conducted. We find that training data duration and train-test delay affect identifiability; that minimal train-test delay leads to very high accuracy; and that train-test delay should be controlled in future experiments.

With the increasing adoption of mixed reality headsets with video passthrough functionality, concerns over perceptual and social effects have surfaced. Building on prior qualitative findings, this study quantitatively investigates the impact of video passthrough on users. Forty participants completed a body transfer task twice, once while wearing a headset in video passthrough and once without a headset. Results indicate that using video passthrough induces simulator sickness, creates social absence, (another person in the physical room feels less present), alters self-reported body schema, and distorts distance perception. On the other hand, compared to past research which showed perceptual aftereffects from video passthrough, the current study found none. We discuss the broader implications for the widespread adoption of mixed reality headsets and their impact on theories surrounding presence and body transfer.

Social virtual reality (VR), by definition, focuses on people, using networked VR systems to bring avatars together. Previous studies have examined how different factors affect social interaction, in small groups such as dyads or triads. However, in a typical social VR scene there tends to be dozens of avatars, even those not directly interacting with a given user. Furthermore, beyond the virtual environment, VR users are also situated in various immediate physical social contexts. In two field experiments, we investigate how the presence of virtual and physical people contextualize and influence nonverbal behaviors. Study 1 examines virtual context and asks how interacting with others in a private or public virtual environment influences nonverbal outcomes during interactions in a social VR platform. Across two sessions, participants (n = 104) met either in a private virtual environment with their group members alone or in a public environment surrounded by four other groups. Results showed that participants moved their avatars slower and stood closer to group members in public versus private environments. Study 2 examines physical context and asks how interacting with virtual others while physically together or alone influences nonverbal behaviors. Participants (n = 61) met in virtual environments while they were in either a shared physical environment or separated physical environments. Results showed that, compared to remote participants, participants who were physically together moved their bodies more slowly, but their avatars faster. Moreover, there was more mutual gaze among remote participants. We discuss implications to theories of social influence in VR.

In networked virtual reality (VR), user behaviors, individual differences, and group dynamics can serve as important signals into 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 gaze (e.g., how much the group looks at the speaker), as well as the listener's 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.