Comparing Working Memory and Verbal Learning in Older Adult Musicians and Non-musicians
Bibliographic record
Abstract
Abstract Previous research suggests that learning or playing an instrument may benefit working memory and executive functioning. The literature also suggests vocal training or singing ability may increase proficiency in verbal learning and working memory. Despite the benefits of musical training, the underlying mechanisms remain unclear. Older adult participants (N=38, Mean age =70.2) provided their music training history and completed a cognitive test battery. Musicians were either instrumentalists and/or vocalists (N=24) or non-musicians (N=14). Independent t test analyses were run with the current modest sample size to compare scores in basic and complex attention and working memory (Digit Span Forward (DSF) and Digit Span Backwards (DSB, and Digit Span Sequencing (DSS)), and verbal learning and memory (California Verbal Learning Test-3 (CVLT)). Results found that musicians/singers had higher scores compared to non-musicians on DSS (t(32)= -1.96, Cohen’s d =.72, p =.058) and on CVLT delayed raw scores(t(32)= -1.98, Cohen’s d=.71, p=.056), both with a medium-large effect size. There were no significant differences found between musicians and non-musicians in DSF and DSB or on CVLT immediate recall/learning. The results suggest that musical training, either instrumental or vocal, may contribute to working memory and verbal memory in older adults. Both the Digit Span task and CVLT rely heavily on executive functioning ability, which may act as a mechanism or mediator between instrumental and vocal training and scores on these cognitive tasks.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".