Accuracy of Subjective Performance Appraisal is Not Modulated by the Method Used by the Learner During Motor Skill Acquisition
Bibliographic record
Abstract
The present experiment examined whether the method of subjectively appraising motor performance during skill acquisition would differentially strengthen performance appraisal capabilities and subsequent motor learning. Thirty-six participants (18 men and 18 women; M age = 20.8 years, SD = 1.0) learned to execute a serial key-pressing task at a particular overall movement time (2550 ms). Participants were randomly separated into three groups: the Generate group estimated their overall movement time then received knowledge of results of their actual movement time; the Choice group selected their perceived movement time from a list of three alternatives; the third group, the Control group, did not self-report their perceived movement time and received knowledge of results of their actual movement time on every trial. All groups practiced 90 acquisition trials and 30 no knowledge of results trials in a delayed retention test. Results from the delayed retention test showed that both methods of performance appraisal (Generate and Choice) facilitated superior motor performance and greater accuracy in assessing their actual motor performance compared with the control condition. Therefore, the processing required for accurate appraisal of performance was strengthened, independent of performance appraisal method.
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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 | 0.012 |
| 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 | 0.001 | 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".