Response of Vestibular Nerve Afferents Innervating Utricle and Saccule During Passive and Active Translations
Bibliographic record
Abstract
The distinction between sensory inputs that are a consequence of our own actions from those that result from changes in the external world is essential for perceptual stability and accurate motor control. In this study, we investigated whether linear translations are encoded similarly during active and passive translations by the otolith system. Vestibular nerve afferents innervating the saccule or utricle were recorded in alert macaques. Single unit responses were compared during passive whole body, passive head-on-body, and active head-on-body translations (vertical, fore-aft, or lateral) to assess the relative influence of neck proprioceptive and efference copy-related signals on translational coding. The response dynamics of utricular and saccular afferents were comparable and similarly encoded head translation during passive whole body versus head-on-body translations. Furthermore, when monkeys produced active head-on-body translations with comparable dynamics, the responses of both regular and irregular afferents remained comparable to those recorded during passive movements. Our findings refute the proposal that neck proprioceptive and/or efference copy inputs coded by the efferent system function to modulate the responses of the otolith afferents during active movements. We conclude that the vestibular periphery provides faithful information about linear movements of the head in the space coordinates, regardless of whether they are self- or externally generated.
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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".