Frequency response of vestibular reflexes in neck, back, and lower limb muscles
Bibliographic record
Abstract
Vestibular pathways form short-latency disynaptic connections with neck motoneurons, whereas they form longer-latency disynaptic and polysynaptic connections with lower limb motoneurons. We quantified frequency responses of vestibular reflexes in neck, back, and lower limb muscles to explain between-muscle differences. Two hypotheses were evaluated: 1) that muscle-specific motor-unit properties influence the bandwidth of vestibular reflexes; and 2) that frequency responses of vestibular reflexes differ between neck, back, and lower limb muscles because of neural filtering. Subjects were exposed to electrical vestibular stimuli over bandwidths of 0-25 and 0-75 Hz while recording activity in sternocleidomastoid, splenius capitis, erector spinae, soleus, and medial gastrocnemius muscles. Coherence between stimulus and muscle activity revealed markedly larger vestibular reflex bandwidths in neck muscles (0-70 Hz) than back (0-15 Hz) or lower limb muscles (0-20 Hz). In addition, vestibular reflexes in back and lower limb muscles undergo low-pass filtering compared with neck-muscle responses, which span a broader dynamic range. These results suggest that the wider bandwidth of head-neck biomechanics requires a vestibular influence on neck-muscle activation across a larger dynamic range than lower limb muscles. A computational model of vestibular afferents and a motoneuron pool indicates that motor-unit properties are not primary contributors to the bandwidth filtering of vestibular reflexes in different muscles. Instead, our experimental findings suggest that pathway-dependent neural filtering, not captured in our model, contributes to these muscle-specific responses. Furthermore, gain-phase discontinuities in the neck-muscle vestibular reflexes provide evidence of destructive interaction between different reflex components, likely via indirect vestibular-motor pathways.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".