Enhanced Complex Mismatch Negativity and Mnemonic Representations in Older Adult Musicians
Bibliographic record
Abstract
Instrumental music performance is associated with enhanced perceptual processing as evidenced by auditory discrimination and speech-in-noise perception. However, little is known about the extent to which auditory perceptual processes support cognition in aging. We investigated whether music training enhances precision in perception in 26 older amateur and professional musicians (62–85 years, 13 females) and 25 older non-musicians (61–82 years, 16 females). Participants completed a novel paradigm of auditory mnemonic discrimination while electroencephalography (EEG) was recorded. The mismatch negativity (MMN), an event-related potential of change detection, was measured during a passive auditory oddball paradigm with standard and deviant pure-tone sequences differing in pitch contour. Participants subsequently completed an incidental memory test for oddball stimuli (i.e., targets) amongst similar lure sequences (matched for frequency but differing in contour) and dissimilar foil sequences (differing in frequency and contour), as well as a back-to-back perceptual discrimination task. Musicians showed enhanced amplitudes, left-lateralized source activity of the MMN, and increased memory discriminability for targets compared to lures and foils, which was not explained by perceptual discrimination ability or MMN amplitude. No group differences were found for neural or behavioural measures on a mnemonic discrimination task for visual everyday objects. Our results clarify the role of music training on precision in perception and auditory memory in older adult musicians compared to non-musicians. Our findings underscore the contribution of musical engagement to perception and memory to the development of cognitive reserve in aging.
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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 | 0.000 |
| 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".