Loss of peripheral vestibular input alters the statistics of head movement experienced during natural self‐motion
Bibliographic record
Abstract
KEY POINTS: Sensory systems are adapted to the statistical structure of natural stimuli, thereby optimizing neural coding. Head motion during natural activities is first sensed and then processed by central vestibulo-motor pathways to influence subsequent behaviour, thereby establishing a feedback loop. To investigate the role of this vestibular feedback on the statistical structure of the head movements, we compared head movements in patients with unilateral vestibular loss and healthy controls. We show that the loss of vestibular feedback substantially alters the statistical structure of head motion for activities that require rapid online feedback control and predict this change by modelling the effects of increased movement variability. Our findings suggest that, following peripheral vestibular loss, changes in the reliability of the sensory input to central pathways impact the statistical structure of head motion during voluntary behaviours. ABSTRACT: It is widely believed that sensory systems are adapted to optimize neural coding of their natural stimuli. Recent evidence suggests that this is the case for the vestibular system, which senses head movement and contributes to essential functions ranging from the most automatic reflexes to voluntary motor control. During everyday behaviours, head motion is sensed by the vestibular system. In turn, this sensory feedback influences subsequent behaviour, raising the questions of whether and how real-time feedback provided by the vestibular system alters the statistical structure of head movements. We predicted that a reduction in vestibular feedback would alter head movement statistics, particularly for tasks reliant on rapid vestibular feedback. To test this proposal, we recorded six-dimensional head motion in patients with variable degrees of unilateral vestibular loss during standard balance and gait tasks, as well as dynamic self-paced activities. While distributions of linear accelerations and rotational velocities were comparable for patients and age-matched healthy controls, comparison of power spectra revealed significant differences during more dynamic and challenging activities. Specifically, consistent with our prediction, head movement power spectra were significantly altered in patients during two tasks that required rapid online vestibular feedback: active repetitive jumping and walking on foam. Using computational methods, we analysed concurrently measured torso motion and identified increases in head-torso movement variability. Taken together, our results demonstrate that vestibular loss significantly alters head movement statistics and further suggest that increased variability and impaired feedback to internal models required for accurate motor control contribute to the observed changes.
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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".