Comparing the effectiveness of stereo projection versus 3D TV in inducing self-motion illusions (vection)
Bibliographic record
Abstract
A necessary part of developing effective and realistic Virtual Reality (VR) simulations is emulating perceptual sensations that occur to humans in corresponding natural environments. VR users are often seated and unable to freely move through the virtual world, therefore necessitating other means to simulate and perceive self-movement. One approach to tackle this challenge is to induce embodied illusions of self-motion ("vection") in stationary observers, typically by providing moving visual stimuli on a wide field-of-view display. While numerous stimulus parameters have been shown to affect vection [see Riecke, 2011 for a review], there is little research investigating how the type of display itself might contribute. Here, we compared the vection-inducing potential as well as user experience and usability of two common displays for large-field stimulation: A passive stereoscopic projection setup and a 3D television with shutter glasses. Uncovering differences in vection between these displays would contribute to the theoretical understanding of vection and the potential relevance of different display properties, and guide the development of more immersive and effective VR setups. From a practical standpoint, this study helps to determine whether the more expensive projection system provides a benefit over the more accessible and affordable 3D television.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".