The effects of awareness of the perturbation during motor adaptation on hand localization
Bibliographic record
Abstract
ABSTRACT Explicit awareness of a task is often evoked during rehabilitation and sports training with the intention of accelerating learning and improving performance. However, the effects of awareness of perturbations on the resulting sensory and motor changes produced during motor learning are not well understood. Here, we use explicit instructions as well as large rotation sizes to generate awareness of the perturbation during a visuomotor rotation task and test the resulting changes in both perceived and predicted sensory consequences as well as implicit motor changes. We split participants into 4 groups which differ in both magnitude of the rotation (either 30° or 60°) during adaptation, and whether they receive a strategy to counter the rotation or not prior to adaptation. Performance benefits of explicit instruction are largest during early adaptation but continued to lead to improved performance through 90 trials of training. We show that with either instruction, or with large perturbations, participants become aware of countering the rotation. However, we find a base amount of implicit learning, with equal magnitudes, across all groups, even when asked to exclude any strategies while reaching with no visual feedback of the hand. Participants also estimate the location of the unseen hand when it is moved by the robot (passive localization) and when they generate their own movement (active localization) following adaptation. These learning-induced shifts in estimates of hand position reflect both proprioceptive recalibration and updates in the predicted consequences of movements. We find that these estimates of felt hand position, which reflect updates in both proprioception and efference based estimates of hand position, shift significantly for all groups and were not modulated by either instruction or perturbation size. Our results indicate that not all processes of motor learning benefit from an explicit awareness of the task. Particularly, proprioceptive recalibration and the updating of predicted sensory consequences are largely implicit processes.
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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 | 0.003 |
| 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".