Cognitive plasticity in older adults: effects of cognitive training and physical exercise
Bibliographic record
Abstract
Cognitive training, physical activity, and exercise have often been reported to improve cognitive performance in older adults. This paper reviews some seminal and recent studies using these approaches to improve cognition and physical functioning in healthy older adults and in patients suffering from non-neurological chronic medical conditions. Results from cognitive training studies suggest that despite performance improvement in trained tasks, transfer effects appeared very limited. Surprisingly though, computerized dual-task training has been shown to improve balance and postural control in tests of physical functioning, suggesting that broad transfer can sometimes be observed. Physical exercise intervention studies generally found significant and large improvements in physical capacity, in some cognitive domains, and in quality of life. The benefits seem to be equivalent between frail and nonfrail participants. Overall, results reviewed here support the notion that cognitive plasticity for attentional control, as induced by cognitive training or physical activity and exercise, is preserved in late adulthood. Moreover, results of studies with patients at risk of cognitive decline also suggest that cognitive training and exercise interventions are promising nonpharmaceutical tools to help improve cognition in older at-risk individuals.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".