Selective Processing of Vestibular Reafference during Self-Generated Head Motion
Bibliographic record
Abstract
The vestibular sensory apparatus and associated vestibular nuclei are generally thought to encode head-in-space motion. Angular head-in-space velocity is detected by vestibular hair cells that are located within the semicircular canals of the inner ear. In turn, the afferent fibers of the vestibular nerve project to neurons in the vestibular nuclei, which, in head-restrained animals, similarly encode head-in-space velocity during passive whole-body rotation. However, during the active head-on-body movements made to generate orienting gaze shifts, neurons in the vestibular nuclei do not reliably encode head-in-space motion. The mechanism that underlies this differential processing of vestibular information is not known. To address this issue, we studied vestibular nuclei neural responses during passive head rotations and during a variety of tasks in which alert rhesus monkeys voluntarily moved their heads relative to space. Neurons similarly encoded head-in-space velocity during passive rotations of the head relative to the body and during passive rotations of the head and body together in space. During all movements that were generated by activation of the neck musculature (voluntary head-on-body movements), neurons were poorly modulated. In contrast, during a task in which each monkey actively "drove" its head and body together in space by rotating a steering wheel with its arm, neurons reliably encoded head-in-space motion. Our results suggest that, during active head-on-body motion, an efferent copy of the neck motor command, rather than the monkey's knowledge of its self-generated head-in-space motion or neck proprioceptive information, gates the differential processing of vestibular information at the level of the vestibular nuclei.
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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 | 0.000 |
| 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".