Influences of the amount of opportunities for KR on self-control strategies and the learning of a spatial-temporal task
Bibliographic record
Abstract
Providing participants with self-control over their KR schedule has proven beneficial for the retention and transfer of motor skills. However, the optimal amount of self-control opportunities required to maintain these learning advantages are unknown. In this study, the number of opportunities to request KR following the performance of a serial response timing task was manipulated. The task was to complete a series of key-pressing responses (1-3-4-2-3-1) in a goal time of 2500 milliseconds. Participants (n=32) were divided into four groups that were provided with self-control over 25%, 50%, 75%, or 100% (SC20, SC40, SC60 or SC80) of the acquisition trials (80). The participants in four yoked groups (n=32) replicated the same feedback schedule as their self-control counterpart, however without the choice. Twenty-four hours after the last acquisition trial, a retention, a time-transfer (i.e., a new goal time: 3300 ms), and a pattern transfer test (i.e., a new pattern in 2500 ms: 2-1-3-1-4-3) were completed. In the retention period, the SC20 group (160ms) was more consistent than their yoked counterparts (246ms), F(1,14)=4.85, 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 | 0.003 |
| 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) | 0.002 | 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".