The human motor system alters its reaching movement plan for task-irrelevant, positional forces
Bibliographic record
Abstract
The minimum intervention principle and the uncontrolled manifold hypothesis state that our nervous system only responds to force perturbations and sensorimotor noise if they affect task success. This idea has been tested in muscle and joint coordinate frames and more recently using workspace redundancy (e.g., reaching to large targets). However, reaching studies typically involve spatial and or temporal constraints. Constrained reaches represent a small proportion of movements we perform daily and may limit the emergence of natural behavior. Using more relaxed constraints, we conducted two reaching experiments to test the hypothesis that humans respond to task-relevant forces and ignore task-irrelevant forces. We found that participants responded to both task-relevant and -irrelevant forces. Interestingly, participants experiencing a task-irrelevant force, which simply pushed them into a different area of a large target and had no bearing on task success, changed their movement trajectory prior to being perturbed. These movement trajectory changes did not counteract the task-irrelevant perturbations, as shown in previous research, but rather were made into new areas of the workspace. A possible explanation for this behavior change is that participants were engaging in active exploration. Our data have implications for current models and theories on the control of biological motion.
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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 | 0.004 |
| 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".