The Accuracy of Subjective Judgments with Motor Learning: Comparison between Young and Elderly People
Bibliographic record
Abstract
Purpose: The purpose of this study was to examine the accuracy of subjective judgments regarding motor learning in the elderly people. Methods: Healthy young adults (n = 14) and healthy older adults (n = 16) participated in this study. Participants were required to reach for a target key without visual information and to learn the location of the target key by using extrinsic visual feedback. Participants performed an initial session that including 20 trials before the learning phase. Then, participants performed three learning blocks, one block consisted of three sessions with 20 trials in each session. In addition, participants were asked to make the following subjective judgments: ease of learning (before performing experimental tasks), judgments of learning (between sessions), and judgment of performance (after completing all the tasks). Results: In both age groups, the success ratio increased with the progress of the task. There was no significant difference in the ease of learning between the two age groups. In younger adults, accuracy of the judgments of learning increased with the progress of the task, whereas this was not the case in older adults. Furthermore, judgment of performance in younger adults was more accurate than that in older adults. Conclusion: These results suggest that the subjective judgment during motor learning in the elderly people is inaccuracy.
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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 | 0.009 |
| 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 | 0.000 |
| 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".