Does Pathological Aging Affect Musical Learning and Memory?
Bibliographic record
Abstract
the effect of pathological aging on explicit memory is very well documented, but relatively few studies have addressed this issue in the musical domain. To examine learning and consolidation of melodies, we designed a melodic recognition task involving immediate and delayed recognition of 16 target melodies (8 familiar and 8 unfamiliar). Seventeen patients with mild to moderate Alzheimer's disease (AD) and 17 age-matched controls were tested. During the initial presentation of the targets, the participant had to decide whether or not the melody was familiar. Recognition was tested after one and three presentations of the target melodies using a yes/no recognition paradigm. Delayed recognition was tested after 24 hours to evaluate consolidation. In keeping with the findings of Bartlett, Halpern, and Dowling (1995), age-matched controls showed better recognition of familiar than unfamiliar melodies. Controls also showed improved performance with multiple presentations for both familiar and unfamiliar melodies, without forgetting after 24-hour delay. In contrast, patients with AD showed impaired learning and recognition of both unfamiliar and familiar melodies with no benefit of familiarity on recognition. Nevertheless, the familiarity decision-based ratings of patients was in keeping with controls. These findings suggest that musical recognition memory is impaired in AD, but the musical lexicon (as assessed by familiarity ratings) is preserved. These findings highlight the need to use both familiar and unfamiliar music in experimental tasks to study the different processes underlying recognition memory.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".