Long-term Maintenance of Retest Learning in Young Old and Oldest Old Adults
Bibliographic record
Abstract
This study examined the maintenance of retest learning benefi ts in young old and oldest old adults over an 8-month period in 3 cognitive abilities: reasoning, perceptual-motor speed, and visual attention. Twenty-four young old (aged 70 – 79 years, M = 74.2) and 23 oldest old adults (aged 80 – 90 years, M = 83.6) who participated in a previously published study (Yang, L., Krampe, R. T., & Baltes, P. B. [2006]. Basic forms of cognitive plasticity extended into the oldest-old: Retest learning, age, and cognitive functioning. Psychology and Aging, 21, 372 – 378) returned after an 8-month delay to complete 2 follow-up retest sessions. The results demonstrated that both young old and oldest old groups maintained about 50% of the original retest learning benefi ts. This extends the earlier fi ndings of substantial long-term cognitive training maintenance in young old adults to a context of retest learning with oldest old adults, and thus portrays a positive message for cognitive plasticity of the oldest old
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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 | 0.002 | 0.005 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".