Inter-Joint Coupling Strategy During Adaptation to Novel Viscous Loads in Human Arm Movement
Bibliographic record
Abstract
When arm movements are perturbed by a load, how does the nervous system adjust control signals to reduce error? While it has been shown that the nervous system is capable of compensating for the effects of limb dynamics and external forces, the strategies used to adapt to novel loads are not well understood. We used a robotic exoskeleton [kinesiological instrument for normal and altered reaching movements (KINARM)] to apply novel loads to the arm during single-joint elbow flexions in the horizontal plane (shoulder rotation was allowed). Loads varied in magnitude with the instantaneous velocity of elbow flexion, and were applied to the shoulder in experiment 1 (interaction loads) and the elbow in experiment 2 (direct loads). Initial exposure to both interaction and direct loads resulted in perturbations at both joints, even though the load was applied to only a single joint. Subjects tended to correct for the kinematics of the elbow joint while perturbations at the shoulder persisted. Electromyograms (EMGs) and computed muscle torque showed that subjects modified muscle activity at the elbow to reduce elbow positional deviations. Shoulder muscle activity was also modified; however, these changes were always in the same direction as those at the elbow. Current models of motor control based on inverse-dynamics calculations and force-control, as well as models based on positional control, predict an uncoupling of shoulder and elbow muscle torques for adaptation to these loads. In contrast, subjects in this study adopted a simple strategy of modulating the natural coupling that exists between elbow and shoulder muscle torque during single-joint elbow movements.
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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 | 0.002 |
| 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) | 0.001 | 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".