Human eye-head gaze shifts preserve their accuracy and spatiotemporal trajectory profiles despite long-duration torque perturbations that assist or oppose head motion
Bibliographic record
Abstract
Humans routinely use coordinated eye-head gaze saccades to rapidly and accurately redirect the line of sight (Land MF. Vis Neurosci 26: 51-62, 2009). With a fixed body, the gaze control system combines visual, vestibular, and neck proprioceptive sensory information and coordinates two moving platforms, the eyes and head. Classic engineering tools have investigated the structure of motor systems by testing their ability to compensate for perturbations. When a reaching movement of the hand is subjected to an unexpected force field of random direction and strength, the trajectory is deviated and its final position is inaccurate. Here, we found that the gaze control system behaves differently. We perturbed horizontal gaze shifts with long-duration torques applied to the head that unpredictably either assisted or opposed head motion and very significantly altered the intended head trajectory. We found, as others have with brief head perturbations, that gaze accuracy was preserved. Unexpectedly, we found also that the eye compensated well--with saccadic and rollback movements--for long-duration head perturbations such that resulting gaze trajectories remained close to that when the head was not perturbed. However, the ocular compensation was best when torques assisted, compared with opposed, head motion. If the vestibuloocular reflex (VOR) is suppressed during gaze shifts, as currently thought, what caused invariant gaze trajectories and accuracy, early eye-direction reversals, and asymmetric compensations? We propose three mechanisms: a gaze feedback loop that generates a gaze-position error signal; a vestibular-to-oculomotor signal that dissociates self-generated from passively imposed head motion; and a saturation element that limits orbital eye excursion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".