Contribution of motion parallax to segmentation and depth perception
Bibliographic record
Abstract
Relative image motion resulting from active movement of the observer could potentially serve as a powerful perceptual cue, both for segmentation of object boundaries and for depth perception. To examine the perceptual role of motion parallax from shearing motion, we measured human performance in three psychophysical tasks: segmentation, depth ordering, and depth magnitude estimation. Stimuli consisted of random dot textures that were synchronized to head movement with sine- or square-wave modulation patterns. Segmentation was assessed with a 2AFC orientation judgment of a motion-defined boundary. In the depth-ordering task, observers reported which modulation half-cycle appeared in front of the other. Perceived depth magnitude was matched to that of a 3D rendered image with multiple static cues. The results indicate that head movement might not be important for segmentation, even though it is crucial for obtaining depth from motion parallax--thus, concomitant depth perception does not appear to facilitate segmentation. Our findings suggest that segmentation works best for abrupt, sharply defined motion boundaries, whereas smooth gradients are more powerful for obtaining depth from motion parallax. Thus, motion parallax may contribute in a different manner to segmentation and to depth perception and suggests that their underlying mechanisms might be distinct.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".