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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".