Contributions of vergence, looming, and relative disparity to the perception of motion in depth
Bibliographic record
Abstract
It is known that modulations of absolute binocular disparity of a textured surface do not create a sensation of motion in depth (MID) when the image does not change size (loom). We reported previously that modulations of disparity do create some MID in a surface containing a radial pattern that lacks a looming signal when it moves in depth. We have built an instrument that allows us to independently control looming, changing absolute disparity (vergence), and changing relative disparity of surfaces actually moving in depth. A textured surface and a surface with a radial pattern moved back and forth in depth between 40 cm and 70 cm. With monocular viewing, looming created MID of the textured display but not of the radial display. Modulation of absolute disparity (vergence) produced no MID of the textured display but some MID of the radial display. When modulation of absolute disparity was increased relative to looming, MID was increased for both displays. When disparity modulation and looming were in conflict, MID decreased for both stimuli. These results indicate cue summation. Superimposition of a stationary reference stimulus that provided changing relative disparity, generally increased MID for both stimuli. Addition of the reference stimulus to the radial display with reversed vergence produced MID in accordance with the vergence signal. Addition of the reference stimulus to the patterned display with vergence reversed relative to looming, produced a paradoxical effect. The textured display appeared to move simultaneously in opposite directions. When it appeared to move forward relative to the observer, it appeared to move backward relative to the stationary reference stimulus. This indicates strong cue dissociation. We will demonstrate this unique paradoxical effect.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".