Sit, stand, and swivel: Posture affects visual exploration of panoramic scenes in virtual reality
Bibliographic record
Abstract
This 45-minute study, composed of 27 participants (20 female, 7 male) from the University of British Columbia (mean age 21.5 years), systematically examined how posture -- sitting in a stationary chair, standing, or swiveling in a chair -- affects visual exploration of immersive virtual environments. Using 360° panoramic scenes, we analysed eye, head, and torso movements to assess the spatial extent and coordination of visual behavior. Standing posture enabled the greatest movement range and scene coverage, while fixed sitting constrained exploration, resulting in compensatory eye-in-head activity. The swivel condition closely approximated standing, suggesting that rotational freedom, not upright posture alone, drives naturalistic gaze behavior. Analyses confirmed that posture significantly shapes horizontal movement distribution, especially for head and torso. Eyes led head and torso movements, revealing a dynamic, nested coordination pattern. These findings, based on the unique integration of high-precision oculomotor data with a systematic comparison of different postures, extend prior work and emphasise posture's critical role in shaping embodied vision in virtual reality. Beyond research design implications, our results inform VR-based physical therapy and immersive skill training, highlighting the need to consider physical movement affordances in immersive contexts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".