Is segmentation from motion parallax influenced by perceived depth?
Bibliographic record
Abstract
A powerful cue for parsing occlusion boundaries arises from relative image motion produced by an observer's self-motion (“motion parallax”), which may also utilize vestibular cues. In this situation, segmentation of adjacent surfaces is intimately associated with their perceived difference in depth. Here we investigate to what extent a depth difference generated from self-motion might aid in segmentation performance. Stimuli appeared within a circular aperature containing a near-horizontal boundary between half-discs that were filled with 1/f (fractal) noise textures. On each trial the observer freely executed lateral head movements, which were measured using a 6-DOF electromagnetic tracking system. The textures' movements were linked to the head movement with no perceptible lag. Three relative motion conditions were compared: the two halves could move in opposite directions, in the same direction at different speeds, in sync with the head movement, or they could both move against the head movement (simulating a pair of surfaces on opposite sides of, farther than, or closer to the fixation point, respectively). On each trial the observer reported whether the moving boundary was slightly oriented left- or right-oblique. A method of constant stimuli was used to measure a boundary orientation threshold. To examine the effect of self-motion, using comparable texture motions, we kept the head fixed with the image regions moving according to trajectories recorded during previous head-moving trials. Orientation thresholds were smaller when the textures were moving oppositely than when moving in the same direction, and similar for the same-direction cases. Overall there was little or no difference in segmentation performance between head-free and head-fixed conditions, even though observers often reported seeing a depth difference. These results suggest that segmentation is a low level mechanism that preceeds depth perception, and that it may not benefit from vestibular input.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".