Torsional dynamics and cross-coupling in the human vestibulo-ocular reflex during active head rotation
Bibliographic record
Abstract
Six subjects fixated an imagined space-fixed target in darkness, or a visible target against a structured visual background, while rotating their heads actively in yaw, pitch and roll at four different frequencies, from 0.3 to 2.4 Hz. We used search coils to measure the 3-dimensional rotations of the head and eye, and described the relation between them--the input-output function of the rotational vestibulo-ocular reflex (VOR)--using gain matrices. We found consistent cross-coupling in which torsional head rotation evoked horizontal eye rotation. The reason may be that the eyes are above the axis of torsional head rotation, and therefore may translate horizontally during the head motion, so the VOR rotates them horizontally to compensate. Torsional gain was lower than horizontal or vertical, more variable from subject to subject and decreased at low frequencies. One reason for the low gain may be that torsional head rotation produces little retinal slip near the fovea; hence little compensatory eye motion is needed, and so the VOR reduces its torsional gain to save energy or to approximate Listing's law by keeping ocular torsion near zero. In addition, the human VOR has little experience with purely torsional head rotations and so its adaptive networks may be poorly trained for such stimuli. The drop in torsional gain at low frequencies can be explained based on the leak in the neural integrator that helps convert torsional eye-velocity commands into eye-position commands.
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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 | 0.000 | 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".