Examining cognitive training and executive function in older adults
Bibliographic record
Abstract
Studies have shown that cognitive functions decline with increasing age. As the population of older adults (OA) has grown, interest in cognitive training programs (CTP) has steadily expanded. The present study investigated whether CTP can lead to improvements in the performance of OA on cognitive tasks. Thirty-five adults (OA; 60-87 years) were recruited to complete 25 sessions of a CTP over five weeks, with assessments completed before and after the program. Thirty-two young adults (YA; 17-27 years) were also recruited to complete one assessment for baseline comparison with OA. During assessments, participants were evaluated using tasks of executive function, including the N-back task of working memory and Flanker task of inhibition. The response time (RT) and hit rates of YA and OA on these tasks were examined at baseline, as well as changes in OA pre- and post-training. Repeated measures analysis of variance indicated a reduction of pre- and post-training RT for the Flanker task. There was no post-training change in RT on the N-back task. While OA hit rates did not change significantly pre- and post-assessment on the Flanker task, they showed increased hit rates post-training in the N-back task. In both tasks, OA and YA hit rates and RT were significantly different, with YA demonstrating lower RT and hit rate compared to OA. Follow-up studies will determine whether other factors can also lead to improvement. Determining whether CTP can improve cognitive performance in OA can help determine the potential of such approaches to prevent or rehabilitate age-related cognitive decline.
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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 | 0.001 |
| 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".