Can walking motions improve visually induced rotational self-motion illusions in virtual reality?
Bibliographic record
Abstract
Illusions of self-motion (vection) can provide compelling sensations of moving through virtual environments without the need for complex motion simulators or large tracked physical walking spaces. Here we explore the interaction between biomechanical cues (stepping along a rotating circular treadmill) and visual cues (viewing simulated self-rotation) for providing stationary users a compelling sensation of rotational self-motion (circular vection). When tested individually, biomechanical and visual cues were similarly effective in eliciting self-motion illusions. However, in combination they yielded significantly more intense self-motion illusions. These findings provide the first compelling evidence that walking motions can be used to significantly enhance visually induced rotational self-motion perception in virtual environments (and vice versa) without having to provide for physical self-motion or motion platforms. This is noteworthy, as linear treadmills have been found to actually impair visually induced translational self-motion perception (Ash, Palmisano, Apthorp, & Allison, 2013). Given the predominant focus on linear walking interfaces for virtual-reality locomotion, our findings suggest that investigating circular and curvilinear walking interfaces offers a promising direction for future research and development and can help to enhance self-motion illusions, presence and immersion in virtual-reality systems.
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".