Depth perception from dynamic occlusion in motion parallax: Roles of expansion-compression versus accretion-deletion
Bibliographic record
Abstract
Motion parallax, or differential retinal image motion from observer movement, provides important information for depth perception. We previously measured the contribution of shear motion parallax to depth, which is only composed of relative motion information. Here, we examine the roles of relative motion and accretion-deletion information in dynamic occlusion motion parallax. Observers performed two-alternative forced choice depth-ordering tasks in response to low spatial frequency patterns of horizontal random dot motion that were synchronized to the observer's head movements. We examined conditions that isolated or combined expansion-compression and accretion-deletion across a range of simulated relative depths. At small depths, expansion-compression provided reliable depth perception while accretion-deletion had a minor contribution: When the two were in conflict, the perceived depth was dominated by expansion-compression. At larger depths in the cue-conflict experiment, accretion-deletion determined the depth-ordering performance. Accretion-deletion in isolation did not yield any percept of depth even though, in theory, it provided sufficient information for depth ordering. Thus, accretion-deletion can substantially enhance depth perception at larger depths but only in the presence of relative motion. The results indicate that expansion-compression contributes to depth from motion parallax across a broad range of depths whereas accretion-deletion contributes primarily at larger depths.
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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) | 0.001 | 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".