Bibliographic record
Abstract
Can self-motion perception in virtual reality (VR) be enhanced by providing affordable, user-powered minimal motion cueing? To investigate this, we compared the effect of different interaction and motion paradigms on onset latency and intensity of self-motion illusions ("vection") induced by curvilinear locomotion in projection-based VR. Participants either passively observed the simulation or had to actively follow pre-defined trajectories of different curvature in a simple virtual scene. Visual-only locomotion (either passive or with joystick control) was compared to locomotion controlled by a modified Gyroxus gaming chair, where leaning forwards and sideways (±10cm) controlled simulated translations and rotations, respectively, using a velocity control paradigm similar to a joystick. In the active visual+chair motion condition, participants controlled the chair motion and resulting virtual locomotion themselves, without the need for external actuation. In the passive visual+chair motion condition, the experimenter did this. Self-motion intensity was increased in the visual+chair motion conditions as compared visual-only motion, corroborating the benefit of simple motion cueing. Surprisingly, however, active control reduced the occurrence of vection and increased vection onset latencies, especially in the chair motion condition. This might be related to the reduced intuitiveness and controllability observed for the active chair motion as compared to the joystick condition. Together, findings suggest that simple user-initiated motion cueing can in principle provide an affordable means of increasing self-motion simulation fidelity in VR. However, usability and controllability issues of the gaming chair used might have counteracted the benefit of such motion cueing, and suggests ways to improve the interaction paradigm.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".