Depth estimation from retinal disparity requires eye and head orientation signals
Bibliographic record
Abstract
To reach for an object, one needs to know its egocentric distance (absolute depth). It remains an unresolved issue which signals are required by the brain to calculate this absolute depth information. We devised a geometric model of binocular 3D eye orientation and investigated the signals necessary to uniquely determine the depth of a non-foveated object accounting for naturalistic variations of eye and head orientations. Our model shows that, in the presence of noisy internal estimates of the ocular vergence angle, horizontal and vertical retinal disparities alone are insufficient to calculate the unique depth of a point-like target. Instead the brain must account for the 3D orientations of the eye and head. We tested the model in a behavioral experiment that involved reaches to targets in depth. Our analysis showed that a target with the same retinal disparity produced different estimates of reach depth that varied consistently with different eye and head orientations. The experimental results showed that subjects accurately account for this extraretinal information when they reach. In summary, when estimating the distance of point-like targets, all available signals about the object's location as well as body configuration are combined to provide accurate information about the object's distance.
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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 | 0.001 | 0.001 |
| 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".