Bilateral Vestibular Loss Leads to Active Destabilization of Balance During Voluntary Head Turns in the Standing Cat
Bibliographic record
Abstract
The purpose of this study was to determine the source of postural instability in labyrinthectomized cats during lateral head turns. Cats were trained to maintain the head in a forward orientation and then perform a rapid, large-amplitude head turn to left or right in yaw, while standing freely on a force platform. Head turns were biomechanically complex with the primary movement in the yaw plane accompanied by an ipsilateral ear-down roll and nose-down pitch. Cats used a strategy of pushing off by activating extensors of the contralateral forelimb while using all four limbs to produce a rotational moment of force about the vertical axis. After bilateral labyrinthectomy, the initial components of the head turn and accompanying postural responses were hypermetric, but otherwise similar to those produced before the lesion. However, near the time of peak yaw velocity, the lesioned cats produced an unexpected burst in extensors of the contralateral limbs that thrust the body to the ipsilateral side, leading to falls. This postural error was in the frontal (roll) plane, even though the primary movement was a rotation in the horizontal (yaw) plane. The response error decreased in amplitude with compensation but did not disappear. We conclude that lack of vestibular input results in active destabilization of balance during voluntary head movement. We postulate that the postural imbalance arises from the misperception that the trunk was rolling contralaterally, based on signals from neck proprioceptors in the absence of vestibular inputs.
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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".