Egocentric distance estimation requires eye-head position signals
Bibliographic record
Abstract
In order to successfully reach to an object presented in the visual field, the brain must reconstruct the egocentric spatial location of this object from available retinal and extraretinal information. The retinal images from both eyes are merged to provide a unique (cyclopean) representation of object direction. Retinal disparity between eyes provides information about the object's distance from the cyclopean eye. The complex geometry of eye-in-head and head-on-body rotations suggests that retinal disparity information of a reach target may not be invariant with regards to gaze (cyclopean eye-in-space) direction. Here, we developed a 3-dimensional (3D) binocular model that incorporates the complete geometry of eye and head rotational positions. We show that different eye-head orientations produce distinct retinal disparities so that, given a target viewed at fixed retinal disparity and cyclopean retinal location, the brain cannot reconstruct target distance without knowledge of eye and head positions. Thus, extraretinal eye and head positions are needed, in addition to retinal disparity and vergence signals, to compute an estimate of distance. This represents the first theoretical evidence showing that the depth component of a desired reach can be accurately computed only if the brain takes into account the linkage geometry of the eye and head. This calculation thus requires a complete 3D visuo-motor reference frame transformation.
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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 | 0.001 | 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".