Eliciting Music-Evoked Autobiographical Memories (MEAMs) in Individuals with Mild Alzheimer’s Disease: A Case Series
Bibliographic record
Abstract
Numerous studies have examined the possibility that listening to music facilitates learning and memory, and various music therapy interventions have yielded promising results for individuals with pathological amnesic disorders. However, the mechanisms by which such phenomena operate are largely unknown. Here I will pursue the hypothesis that music processing pathways are linked to autobiographical memory networks, allowing the spontaneous activation of memories for personal events (music-evoked autobiographical memories, or MEAMs). These reminiscences are often of a pleasant or nostalgic nature, inducing a positive affective state. These emotions, in turn, elicit a sense of identity and self-awareness that contributes to psychological well-being. I will present data from a series of participants diagnosed with mild Alzheimer’s disease, all of whom have varying degrees of short-term memory deficits and cognitive impairments. After listening to a 30-second musical excerpt (in a total sequence of 24), they were asked to describe any memories that may have been conjured by the music, as well as their overall attitude, familiarity, and emotional reaction towards the piece. The experimental stimuli included 12 pieces from a well-known repertory of instrumental classical music, which were matched to 12 similar excerpts controlled for instrumentation, tempo, mode, and stylistic patterns. I video-recorded the interviews, analyzing both verbal and behavioural responses to the musical excerpts played. Data will be compared with that obtained from healthy controls. I will then discuss the implications of the current pilot project towards future studies and potential applications in clinical settings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".