Self-Control of Feedback During Motor Learning: Accounting for the Absolute Amount of Feedback Using a Yoked Group With Self-Control Over Feedback
Bibliographic record
Abstract
A traditional control group yoked to a group that self-controls their reception of feedback receives feedback in the same relative and absolute manner. This traditional control group typically does not learn the task as well as the self-control group. Although the groups are matched for the amount of feedback they receive, the information is provided on trials in which the individual may not request feedback if he or she were provided the opportunity. Similarly, individuals may not receive feedback on trials for which it would be a beneficial learning experience. Subsequently, the mismatch between the provision of feedback and the potential learning opportunity leads to a decrement in retention. The present study was designed to examine motor learning for a yoked group with the same absolute amount of feedback, but who could self-control when they received feedback. Increased mental processing of error detection and correction was expected for the participants in the yoked self-control group because of their choice to employ a limited resource in the form of a decreasing amount of feedback opportunities. Participants in the yoked with self-control group committed fewer errors than the self-control group in retention and the traditional yoked group in both the retention and time transfer blocks. The results suggest that the yoked with self-control group was able to produce efficient learning effects and can be a viable control group for further motor learning studies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".