Rapid Adaptation to Scaled Changes of the Mechanical Environment
Bibliographic record
Abstract
We investigated adaptation to simple force field scaling to determine whether the same strategy is used as during adaptation to more complex changes in the mechanical environment. Subjects initially trained in a force field, consisting of a rightward lateral force with a parabolic spatial profile (PF). The field strength was then unexpectedly increased or decreased (DeltaPF) for repeated sets of five consecutive trials, with intervening PF trials. Stiff elastic walls, which prevented lateral movement of the arm, randomly replaced 25% of DeltaPF trials. Lateral deviation on DeltaPF trials and lateral force against the elastic walls were used to assess the extent to which feedforward adaptations could be attributed to changes in lateral force or increased stiffness of the arm. When force field strength was increased or decreased hand paths were perturbed to the right or left, respectively. Performance error was significantly reduced between the first and second DeltaPF trial positions of the set, whereas the lateral force impulse exerted against the elastic walls did not change until the third trial position. The lateral force was scaled upward or downward in response to the change in force field strength, suggesting a gradual change in the internal model. The results support a dual strategy of cocontraction (increased stiffness) and internal model modification. The development of an accurate internal model is a slower process than cocontraction and error reduction. This may explain the need to represent motor learning as two parallel processes with varying timescales, as recently proposed by Smith and colleagues.
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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".