Fast feedback control involves two independent processes utilizing knowledge of limb dynamics
Bibliographic record
Abstract
Corrective muscle responses occurring 50-100 ms after a mechanical perturbation are tailored to the mechanical features of the limb and its environment. For example, the evoked response by the shoulder's extensor muscle counters an imposed shoulder torque, rather than local shoulder motion, by integrating motion information from the shoulder and elbow appropriate for their dynamic interaction. Previous studies suggest that arm muscle activity within this epoch, alternately termed the R2/3 response, or long-latency reflex, reflects the summed result of two distinct components: an activity-dependent component which scales with the background muscle activity, and a task-dependent component which scales with the required vigor of the behavioral task. Here we examine how the knowledge of limb dynamics expressed during the shoulder muscle's R2/3 epoch is related to these two functional components. Subjects countered torque steps applied to their shoulder and/or elbow under different conditions of background torque and target size to recruit the activity-dependent and task-dependent component in varying degrees. Experiment 1 involved four torque perturbations, two levels of background torques and two target sizes; this design revealed that both background torque and target size impacted the shoulder's R2/3 activity, indicative of knowledge of limb dynamics. Experiment 2 involved two perturbation torques, five levels of background torque and two target sizes; this design demonstrated that the two factors had an independent impact on the R2/3 activity indicative of knowledge of limb dynamics. We conclude that a sophisticated feature of upper limb feedback control reflects the summation of two processes having a common capability.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".