Increasing motion parallax gain compresses space and 3D object shape
Bibliographic record
Abstract
When moving about the world, humans rely on visual, proprioceptive and vestibular cues to perceive depth and distance. Normally, these sources of information are consistent. However, what happens if we receive conflicting information about how far we have moved? A previous study reported that at distances of 1.3 to 1.5 m, portrayed binocular 3D shape was not affected by motion gain; however, apparent distance and monocular depth settings were influenced. In our study, we extended the range of distances to 1.5 to 6 m. A VR headset was used to display gain distortions binocularly and monocularly to one eye. Observers swayed from side to side through 20 cm at 0.5 Hz to the beat of a metronome. The simulated virtual motion was varied by a gain of 0.5 to 2.0 times the physical motion. Observers first adjusted a vertical fold until its sides appeared to form a 90-degree angle. The fold then disappeared and they indicated its remembered distance by adjusting the position of a virtual pole. In the monocular condition as gain increased, observers provided increasingly compressed fold depth settings at 1.5 and 3 but not at 6 m. Under binocular viewing, increasing gain compressed distance but not object shape settings. To ensure that the weak binocular effects were not due to failure to perceive the gain, we separately assessed gain discrimination thresholds using the fold stimulus. We found that observers were sensitive to the manipulation over this range and tended to perceive a gain of 1.1 as having no motion distortion under both viewing conditions. It is clear from our data that monocular viewing of kinesthetic/visual mismatch results in significant variations in portrayed depth of the fold. These effects can be somewhat mitigated by increasing viewing distance, but even more so by viewing with both eyes.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".