Working memory training and transfer in older adults: Effects of age, baseline performance, and training gains.
Bibliographic record
Abstract
Recent studies suggest that working memory training may benefit older adults; however, findings regarding training and transfer effects are mixed. The current study aimed to investigate the effects of a process-based training intervention in a diverse sample of older adults and explored possible moderators of training and transfer effects. For that purpose, 80 older adults (65-95 years) were assigned either to a training group that worked on visuospatial, verbal, and executive working memory tasks for 9 sessions over 3 weeks or to a control group. Performance on trained and transfer tasks was assessed in all participants before and after the training period, as well as at a 9-month follow-up. Analyses revealed significant training effects in all 3 training tasks in trained participants relative to controls, as well as near transfer to a verbal working memory task and far transfer to a fluid intelligence task. Encouragingly, all training effects and the transfer effect to verbal working memory were stable at the 9-month follow-up session. Further analyses revealed that training gains were predicted by baseline performance in training tasks and (to a lesser degree) by age. Gains in transfer tasks were predicted by age and by the amount of improvement in the trained tasks. These findings suggest that cognitive plasticity is preserved over a large range of old age and that even a rather short training regime can lead to (partly specific) training and transfer effects. However, baseline performance, age, and training gains moderate the amount of plasticity.
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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 | 0.004 |
| 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) | 0.001 | 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".