Video speed demonstration under mixed-modeling conditions does not influence learning of a novel motor skill
Bibliographic record
Abstract
Speed of a video demonstration has been manipulated by few researchers, mainly with an interest in slow-motion versus real-time. These few experiments yielded conflicting evidence regarding the benefits of slow-motion video. Further, video demonstration speed research has only included observation of a single model type; however, recent research has shown learning advantages for the use of mixed-models over that of single model types. Given this, and the contradictory findings concerning video speed, the aim here was to explore the effects of slow-motion video demonstration under mixed-modeling conditions (skilled model plus self-observation) on the learning of a novel motor skill. Fifty-one participants were tasked with learning a pirouette-en-dehors while assigned to one of three groups with different mixed-model observation video speeds: (1) slow-motion (2) real-time, or (3) a combination of slow-motion and real-time. Following a pre-test, participants received 72 practice trials, divided into eight blocks, comprised of five physical and four observational practice trials. Physical performance and cognitive representation assessments were completed at pre-test, after even-numbered blocks, and at a 24-hour post-test. Participants' scores improved for both physical performance and cognitive representation assessments 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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".