The influence of adaptive schedules on motor learning in young adults
Bibliographic record
Abstract
Learning is facilitated when practice schedules are adapted to a learner's performance compared to equivalent amounts of random practice (Choi et al. 2008). Task switching could either be sensitive to the learner's actual performance or act as a reward for successful goal achievement. We examined this by comparing two groups that were either rewarded for goal achievement with a task switch or a task repetition. Participants learned to perform four spatially distinct key-press patterns as fast as possible and without error through a discovery process. Their goal was to beat their best movement time (MT) for each specific pattern. For example, when a learner in the WinShift (WS) schedule achieved success (a "win") they switched to a different pattern; failure to beat their best time resulted in immediate repetition of the same pattern. The opposite contingency was used in the WinRepeat (WR) group. The results for both MT and errors in retention tests performed on the same day as practice and the next day were significantly better for the WR group, where a "win" was rewarded with a task repetition. Analysis of the acquisition data revealed that both groups "won" frequently early and less frequently later in practice. This meant that later in acquisition the WS group had mostly blocked practice while the WR group had mostly random practice. These findings reveal that the effectiveness of adaptive practice depends on the nature of contextual interference promoted by the algorithm. Acknowledgments: This study was funded by NSERC.
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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 | 0.000 | 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".