Is an Internal Model of Head Orientation Necessary for Oculomotor Control?
Bibliographic record
Abstract
In order to test whether the control of eye movement in response to head movement requires an internal model of head orientation or instead can rely on directly sensing information about head orientation and movement, perceived gravity was separated from physical gravity to see which dominated the eye-movement response. Internal model theory suggests that the oculomotor response should be driven by perceived, internalized gravity, whereas the direct sensing theory predicts it should always be driven by vestibularly sensed gravity. Subjects lay on an airbed either supine or on their side and were sinusoidally translated along their dorsoventral body axis. The direction of perceived gravity was separated from physical gravity by performing the experiments in a room built on its side with the direction of its "floor" orthogonal to both physical gravity and the subject's translation. The swinging sum of the imposed sinusoidal acceleration with physical gravity was thus in a plane orthogonal to its sum with perceived gravity. Oculomotor responses to these swinging vectors were looked for and responses were found only to the sum of the acceleration with physical gravity, not perceived gravity. It was concluded that an internal model is not used to drive these compensatory eye movements.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".