Observation of a skilled model in a self-controlled learning environment facilitates learning of a novel motor skill irrespective of frequency of modeling
Bibliographic record
Abstract
Observation of a model has been shown to facilitate motor learning, yet the optimal frequency of modeling combined with physical practice has not been well studied. Under an experimenter-controlled learning environment, an alternating schedule of one physical practice trial followed by one observational practice trial (100% frequency) was shown to be the most effective, with a 10% frequency providing no learning gains. When participants self-controlled the scheduling of observational practice, however, participants selected a 10% frequency schedule but still yielded the same observational learning benefits as those in the experimenter-imposed 100% group. Due to these conflicting results, the aim here was to explore whether higher self-controlled observation frequencies would generate greater learning. Forty-eight participants were tasked with learning the pirouette en dehors while assigned to one of four groups with differing constrained self-controlled observation frequencies: (1) 25%, (2) 50%, (3) 75%, or (4) no constraint imposed. Participants received 60 practice trials divided into four blocks of 15. Physical performance assessments were completed at pre-test, after acquisition blocks 1, 2, 3, and 4, and at a 24-hour post-test. Participants' performance increased throughout acquisition (p
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".