When Melodies Cue Memories: Electrophysiological Correlates of Autobiographically Salient Music Listening in Older Adults
Bibliographic record
Abstract
Abstract Autobiographical memory is essential for older adults, providing a foundation for self-identity. Although healthy aging is accompanied by changes in memory retrieval, musical memory remains relatively unaffected, suggesting music may serve as a cue for autobiographical memory recall. Autobiographically salient (ABS) music (i.e., deeply encoded songs associated with significant people, places, and events) is posited to engage distinct memory processes from familiar (FAM) music (i.e., songs that are recognized but lack personal significance). We tested this in 36 older adults (70.6 $$\pm$$ ± 6.6 years, 20 females) who listened to music varying by personal significance, including ABS, FAM, and unfamiliar (UFAM) music. In Experiment 1, participants pressed a button as soon as they identified the excerpt as ABS, FAM, or UFAM. In Experiment 2, we measured event-related potentials and time-frequency responses while participants listened to the same stimuli and rated familiarity and memory following each excerpt. Reaction times were fastest for ABS, followed by FAM, then UFAM music. We observed a sustained evoked response from 2238 to 5000 ms post-stimulus onset that was least negative in amplitude for ABS, relative to FAM and UFAM music, over right frontal-central regions. We also observed less beta power suppression for ABS than FAM music between 1300 and 5000 ms over bilateral frontal-central-parietal areas. Our behavioral and neurophysiological findings show that ABS music elicits faster and distinct memory-related neural activity throughout the excerpts compared to FAM music. These results emphasize that music is a powerful cue for activating memory processes, which varies by personal significance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 | 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".