Multijoint error compensation mediates unstable object control
Bibliographic record
Abstract
A key feature of skilled object control is the ability to correct performance errors. This process is not straightforward for unstable objects (e.g., inverted pendulum or "stick" balancing) because the mechanics of the object are sensitive to small control errors, which can lead to rapid performance changes. In this study, we have characterized joint recruitment and coordination processes in an unstable object control task. Our objective was to determine whether skill acquisition involves changes in the recruitment of individual joints or distributed error compensation. To address this problem, we monitored stick-balancing performance across four experimental sessions. We confirmed that subjects learned the task by showing an increase in the stability and length of balancing trials across training sessions. We demonstrated that motor learning led to the development of a multijoint error compensation strategy such that after training, subjects preferentially constrained joint angle variance that jeopardized task performance. The selective constraint of destabilizing joint angle variance was an important metric of motor learning. Finally, we performed a combined uncontrolled manifold-permutation analysis to ensure the variance structure was not confounded by differences in the variance of individual joint angles. We showed that reliance on multijoint error compensation increased, whereas individual joint variation (primarily at the wrist joint) decreased systematically with training. We propose a learning mechanism that is based on the accurate estimation of sensory states.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".