Dynamic perspective cues enhance depth perception from motion parallax
Bibliographic record
Abstract
Motion parallax, the perception of depth resulting from an observer's self-movement, has almost always been studied with random dot textures in simplified orthographic rendering. Here we examine depth from motion parallax in more naturalistic conditions using textures with an overall 1/f spectrum and dynamic perspective rendering. We compared depth perception for orthographic and perspective rendering, using textures composed of two types of elements: random dots and Gabor micropatterns. Relative texture motion (shearing) with square wave corrugation patterns was synchronized to horizontal head movement. Four observers performed a two-alternative forced choice depth ordering task with monocular viewing, in which they reported which part of the texture appeared in front of the other. For both textures, depth perception was better with dynamic perspective than with orthographic rendering, particularly at larger depths. Depth ordering performance with naturalistic 1/f textures was slightly lower than with the random dots; however, with depth-related size scaling of the micropatterns, performance was comparable to that with random dots. We also examined the effects of removing each of the three cues that distinguish dynamic perspective from orthographic rendering: (a) small vertical displacements, (b) lateral gradients of speed across the corrugations, and (c) speed differences in rendered near versus far surfaces. Removal of any of the three cues impaired performance. In conclusion, depth ordering performance is enhanced by all of the dynamic perspective cues but not by using more naturalistic 1/f textures.
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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 | 0.001 |
| 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".