Tradeoffs in optimal control capture patterns of human sensorimotor control and adaptation
Bibliographic record
Abstract
Abstract Modern control theory highlights strategies that consider a range of factors, such as errors caused by environmental disturbances or inaccurate estimates of body or environmental dynamics. Here we reveal similar diversity in how humans naturally adapt and control their arm movements. We divided participants into groups based on how well they adapted to interaction loads during a single session of reaching movements. This classification revealed differences in how participants controlled their movements and responded to mechanical perturbations. Interestingly, variation in behaviour across good and partial adapters resembled simulations from stochastic and robust optimal feedback control, respectively, where the latter minimizes the effect of disturbances, including those introduced by inaccurate internal models of movement dynamics. In a second experiment, we varied the interaction loads over short time periods making it difficult to adapt. Under these conditions, participants who otherwise adapted well altered their behaviour and more closely resembled those using a robust control strategy. Taken together, the results suggest the diversity of how humans control and adapt their arm movements may reflect the accuracy of (or confidence in) their internal models. Our findings may open novel perspectives for interpreting motor behaviour in uncertain environments, or when neurologic dysfunction compromises motor adaptation.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".