Self-controlled practice: Learning differences between younger and older adults
Bibliographic record
Abstract
Examining the usefulness of a self-controlled KR context in older adults has received little attention in the motor learning literature and thus remains inconclusive. In this experiment, participants pushed and released a low friction slider to a goal distance of 133cm. Participants were randomly assigned to a self-controlled or yoked practice condition resulting in four experimental groups (SELF-OLD, SELF-YOUNG, YOKED-OLD, YOKED-YOUNG). The retention data showed the SELF-YOUNG group (M=9.08, SD=4.56) performed with less |CE| than the SELF-OLD (M=22.02, SD=9.41), YOKED-YOUNG (M=18.79, SD=8.39) and YOKED-OLD (M=22.83, SD=3.93) groups. We were also interested in determining if a self-controlled KR context would develop a participant's error detection and correction ability. Thus, participants were asked to estimate their perceived outcome of each motor response in the immediate and delayed retention tests. The results showed the SELF-YOUNG group (M=6.53, SD=3.53) was more accurate in estimating their motor performance than the SELF-OLD (M=15.58, SD=4.40), YOKED-YOUNG (M=11.43, SD=2.59) and YOKED-OLD (M=16.17, SD=4.27) groups. Our results suggest that older adults do not demonstrate the same learning benefits as younger adults in a self-controlled KR context and the ability to accurately estimate performance also seems to be age dependent. The self-reported KR strategies and the intrinsic sources of feedback utilized by participants will be discussed to account for our findings.Acknowledgments: Canadian Institutes 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".