Comparison of Cognitive Change after Working Memory Training and Logic and Planning Training in Healthy Older Adults
Bibliographic record
Abstract
Recent attention has focused on the benefits of cognitive training in healthy adults. Many commercial cognitive training programs are available given the attraction of not only bettering one's cognitive capacity, but also potentially preventing age-related declines, which is of particular interest to older adults. The issue of whether cognitive training can improve performance within cognitive domains not trained (i.e., far transfer) is controversial, with meta-analyses of cognitive training both supporting and falsifying this claim. More support is present for the near transfer (i.e., transfer in cognitive domain trained) of cognitive training; however, not in all studies. To date, no studies have compared working memory training to training higher-level processes themselves, namely logic and planning. We studied 97 healthy older adults above the age of 65. Healthy older adults completed either an 8-week web-based cognitive training program on working memory or logic and planning. An additional no-training control group completed two assessments 8-weeks apart. Participants were assessed on cognitive measures of near and far transfer, including working memory, planning, reasoning, processing speed, verbal fluency, cognitive flexibility, and creativity. Participants improved on the trained tasks from the first day to last day of training. Bayesian analyses demonstrated no near or far transfer effects after cognitive training. These results support the conclusion that performance-adaptive computerized cognitive training may not enhance cognition in healthy older adults. Our lack of findings could be due to a variety of reasons, including studying a cohort of healthy older adults that were performing near their cognitive ceiling, employing a training protocol that was not sufficient to produce a change, or that no true findings exist. Research suggests numerous study factors that can moderate the results. In addition, the role of psychological variables, such as expectations and motivation to train, are critical in understanding the effects of cognitive training.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".