Cortex-independent open-loop control of a voluntary orofacial motor action
Bibliographic record
Abstract
Abstract Whether using our eyes or our hands, we interact with our environment through mobile sensors. The efficient use of these sensory organs implies the ability to track their position; otherwise, perceptual stability and prehension would be profoundly impeded 1,2 . The nervous system may be informed about the position of a sensory organ via two complementary feedback mechanisms: peripheral reafference (external, sensory feedback) and efference copy (internal feedback) 3-6 . Yet, the potential contributions of these mechanisms remain largely unexplored. By training rats to place their vibrissae within a predetermined angular range without contact, a task that depends on knowledge of vibrissa position relative to their face, we found that peripheral reafference is not required. The presence of motor cortex is not required either, even in the absence of peripheral reafference. On the other hand, the red nucleus, which receives descending inputs from motor cortex and the cerebellum and projects to facial motoneurons 7-10 , is critical for the execution of the vibrissa task. All told, our results demonstrate the existence of an open-loop control by an internal model that is sufficient to drive voluntary motion. The internal model is independent of motor cortex and likely contains the cerebellum and associated nuclei.
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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) | 0.002 | 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".