Visual-Vestibular Interaction Hypothesis for the Control of Orienting Gaze Shifts by Brain Stem Omnipause Neurons
Bibliographic record
Abstract
Models of combined eye-head gaze shifts all aim to realistically simulate behaviorally observed movement dynamics. One of the most problematic features of such models is their inability to determine when a saccadic gaze shift should be initiated and when it should be ended. This is commonly referred to as the switching mechanism mediated by omni-directional pause neurons (OPNs) in the brain stem. Proposed switching strategies implemented in existing gaze control models all rely on a sensory error between instantaneous gaze position and the spatial target. Accordingly, gaze saccades are initiated after presentation of an eccentric visual target and subsequently terminated when an internal estimate of gaze position becomes nearly equal to that of the target. Based on behavioral observations, we demonstrate that such a switching mechanism is insufficient and is unable to explain certain types of movements. We propose an improved hypothesis for how the OPNs control gaze shifts based on a visual-vestibular interaction of signals known to be carried on anatomical projections to the OPN area. The approach is justified by the analysis of recorded gaze shifts interrupted by a head brake in animal subjects and is demonstrated by implementing the switching mechanism in an anatomically based gaze control model. Simulated performance reveals that a weighted sum of three signals: gaze motor error, head velocity, and eye velocity, hypothesized as inputs to OPNs, successfully reproduces diverse behaviorally observed eye-head movements that no other existing model can account for.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".