A Three‐Channel Model for Generating the Vestibulo‐Ocular Reflex in Each Eye
Bibliographic record
Abstract
channels; passive rotation Coding head movement involves representing the head’s velocity and axis of rota-tion. The neural representation can then be used to inform perceptual and motor pro-cesses. An important motor response to head movement is the compensatory eye movements evoked, one component of which is the vestibulo-ocular reflex (VOR). Historically a three-neuron arc has been described as the core of the neural mecha-nism underlying the generation of the VOR.1,2 Such a direct line between sensor (the canals) and effector (the eye muscles) implies independent processing of the geo-metric components of the three-dimensional VOR.3 A more flexible and robust rep-resentation of the movement involves an interactive process in which the activity coding movement in each direction is interpreted in the context of the activity of the others. Many sensory attributes are coded by the activity of a small set of channels,4 and the closely constrained three-dimensional movement of the head could be effi-ciently represented by such a system. Psychophysical methods have been developed to investigate channel systems among which is adaptation. After adapting the re-sponse to a particular stimulus, the effect on the responses to closely related stimuli can often reveal a channel-coding system.5,6 Here we use an adaptation technique to provide evidence for a three-channel model underlying the representation of head ro-tation and generating the vestibulo-ocular reflex of each eye. These channels are conceptually different from those proposed for coding head velocity ranges,7 as dis-cussed elsewhere.8
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".