Goal-dependent modulation of the long-latency stretch response accounts for orientation of the arm
Bibliographic record
Abstract
We recently had participants complete goal-directed reaches following mechanical elbow perturbations that displaced the hand towards or away from a target. Perturbations that displaced the hand away from the target increased the long-latency stretch response (muscle activity 50-100 ms following a perturbation: LLSR) from the stretched elbow muscle as well as from the wrist muscle that assisted moving the hand to the target. This coordinated goal-dependent modulation across multiple muscles suggests that sensory information is rapidly used to support the demands of the intended goal-directed action. Here, we tested whether the LLSR of wrist muscles would reflect the orientation of the arm in the horizontal plane (i.e., thumb up: TU; thumb down: TD). Participants reached to targets in both arm orientations following elbow perturbations that moved their hand into or away from the target. Notably, TU or TD orientations governed the wrist muscle that assisted moving the hand to the target. We found that flexion perturbations that moved the hand away from the target, compared to towards the target, resulted in larger LLSR from wrist extensor and wrist flexor muscles when the arm was in the TU and TD orientation, respectively. These results indicate that the rapid processing of sensory information accounts for configuration of the body relative to the movement-goal and provides further evidence that sensory information is rapidly and flexibly used to support the production of goal-directed actions. Acknowledgments: NSERC
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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".