Dissociating Self-Generated from Passively Applied Head Motion: Neural Mechanisms in the Vestibular Nuclei
Bibliographic record
Abstract
The ability to distinguish 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. To accomplish this, it has been proposed that an internal prediction of the consequences of our actions is compared with the actual sensory input to cancel the resultant self-generated activation. Here, we provide evidence for this hypothesis at an early stage of processing in the vestibular system. Previous studies have shown that neurons in the vestibular nucleus, which receive direct inputs from vestibular afferent fibers, are responsive to passively applied head movements. However, these same neurons do not reliably encode head velocity resulting from self-generated movements of the head on the body. In this study, we examined the mechanism that underlies the selective elimination of vestibular sensitivity to active head-on-body rotations. Individual neurons were recorded in monkeys making active head movements. The correspondence between intended and actual head movement was experimentally controlled. We found that a cancellation signal was gated into the vestibular nuclei only in conditions in which the activation of neck proprioceptors matched that expected on the basis of the neck motor command. This finding suggests that vestibular signals that arise from self-generated head movements are inhibited by a mechanism that compares the internal prediction of the sensory consequences by the brain to the actual resultant sensory feedback. Because self-generated vestibular inputs are selectively cancelled early in processing, we propose that this gating is important for the computation of spatial orientation and control of posture by higher-order structures.
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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) | 0.000 | 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 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".