arXiv subjects

Portia Wang

Publications and source records attributed to Portia Wang.

3 recordsLinked to original sources

arXiv2026

Unpacking the process of watching a 360-degree video: How physiological responses and head rotation relate to affective and cognitive outcomes

A defining feature of 360-degree video is that the perceptual experience is actively constructed. As viewers turn their heads, shift their gaze, and encounter changing visuals, their perceptual experience is not predetermined, but rather emerges through ongoing exploration. Yet, it is unclear how the process of watching immersive videos unfolds, and how it is related to psychological and physiological outcomes. We present a large-scale study (N=200) in which participants experienced one of 10 pieces of 360-degree videos in virtual reality. Results showed that greater head rotation was associated with greater valence, higher attention focus, longer length of subjective review, and lower simulator sickness; greater pupil dilation was associated with lower valence, lower review ratings, and greater use of negative tone in written reviews. A mediation analysis revealed that participants who rotated their heads more experienced greater frame-to-frame pixel-level perceptual changes in their field of view, which in turn resulted in greater valence and lower simulator sickness. Moreover, participants tended to rotate their heads toward field of view regions with greater perceptual movement, while directing their eye gaze toward less perceptually dynamic areas within that view. We outline theoretical and practical contributions for designing 360-degree content and predicting experiential outcomes.

cs.HC↗

arXiv2026

Exploring spatial configuration and group familiarity when co-watching 360-degree stories in social virtual reality

While traditional cinematic viewing is often characterized as a social experience, engaging with immersive stories is predominantly researched as an individual activity. This mismatch becomes increasingly problematic in social virtual reality (VR), where features designed for typical social activities (e.g., virtual locomotion, spatial and social context) can become meaningful levers for shaping the user experience. To address our limited understanding of these effects, we conducted three exploratory experiments (N$_1$=109, N$_2$=116, N$_3$=88) examining how spatial boundary type, size of allocated personal space, and group familiarity influence co-watching 360-degree stories in social VR. Results shows that small communal spatial boundaries heightened social presence towards others compared to individual boundaries, but also increased mental demand. Reducing the size of the personal spatial boundary increased social presence towards others but also increased disorientation and frustration. Finally, participants with higher group familiarity reported stronger attentional focus on the story, as well as higher spatial presence and social presence towards story characters, but also reported greater physical and perceptual demand. We discuss systematic tradeoffs between engagement and cognitive effort, and suggest practical guidelines for designing shared immersive narrative experiences.

cs.HC↗

arXiv2025

Predicting and Understanding Turn-Taking Behavior in Open-Ended Group Activities in Virtual Reality

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.

cs.HC↗