Self-controlled KR: Does it facilitate an internal representation of a spatial motor task?
Bibliographic record
Abstract
Self-controlled KR practice has revealed that participants provided the opportunity to control their KR is superior for motor skill acquisition compared to participants replicating the KR schedule of a self-control participant without the choice (e.g., yoked). To date, the learning advantages of a self-controlled KR schedule have utilized motor tasks requiring acquisition of a temporal goal (e.g., push 5-keys in 1050ms). In the present experiment, we examined the utility of self-controlled KR practice for the acquisition of a motor skill with a spatial goal and whether self-controlled KR practice facilitates the development of an accurate internal representation of the task goal. Twenty-four younger adults were required to push and release a handle along a confined pathway using their non-dominant hand to a target distance (133cm). The self-controlled participants controlled their receipt of KR after every acquisition trial while the yoked participants replicated the KR schedule of a self-controlled participant. The retention data revealed the SELF condition (M=10.73) demonstrated less |CE| compared to the yoked condition (M=17.79) in achieving the spatial goal. As well, the SELF condition was more accurate in estimating their performance (indexed by AD) during the retention period. The findings suggest self-controlled KR practice generalizes to spatial goals and facilitates the development of an accurate internal representation, as evidenced by participant self-reports.Acknowledgments: This research was supported by the Canadian Institute of Health Research
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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 | 0.002 |
| 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 | 0.000 |
| 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".