Subcortical control of reaching in humans
Bibliographic record
Abstract
ABSTRACT Accurate visually guided reaching requires transformation of target-related photoreceptor responses into precisely coordinated activation of trunk and arm muscles. The cerebral cortex is widely believed to compute the requisite kinematic and musculoskeletal dynamics strategies in humans 1–3 , even though vertebrates lacking a cerebral cortex achieve sophisticated visuomotor control 4–6 , and brainstem circuits executing coordinated eye and head gaze shifts perform analogous sensorimotor computations in non-human primates 7 . Here we used a visuomotor reaching task that yields extremely rapid, “express”, target-directed muscle activations 8–10 to test whether a putative subcortical sensorimotor network can compute musculoskeletal dynamics to initiate reaching in humans. We found coordinated express visuomotor responses (EVRs) in task-relevant shoulder, elbow, and bi-articular muscles that reflected both starting posture and target direction in similar patterns to longer latency, presumably cortically mediated, muscle responses. When the task goal was to reach away from the stimulus (i.e. an “anti-reach”; 11 ) the EVR involved coordinated muscle activation to initiate the hand toward the stimulus location, opposite to the subsequent goal-directed response. The results suggest a unified theory of visuomotor control for reaching and gaze shifts, in which subcortical systems compute musculoskeletal dynamics based on sensory target information and cortically derived context. The results imply that the transformation from motor goals in extrapersonal space into musculoskeletal dynamics can be performed by neural circuitry in humans that does not involve the sensorimotor cortex.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".