Music-evoked autobiographical memories in everyday life
Bibliographic record
Abstract
Music can be a particularly effective cue for bringing one back to the sights and sounds of events from across the lifespan. These music-evoked autobiographical memories (MEAMs) have typically been studied within laboratory experiments and clinical settings, often using experimenter-selected music to cue autobiographical memories. The present work took a more naturalistic approach, by studying the situational aspects, contents, and features of MEAMs within the course of participants’ everyday lives. Participants ( N = 31) recorded details of their MEAMs and music listening habits in a diary for 7 days. MEAMs were experienced, on average, once per day and were cued by a wide variety of music, often during routine tasks such as traveling and housework. Everyday MEAMs were typically rated as highly vivid and involuntary and were often accompanied by positive or mixed emotions (e.g., happiness, nostalgia) and social themes. Some evidence of individual differences was found, with older participants rating their MEAMs as more vivid and accompanied by more positive emotions. The features reported within everyday MEAMs replicated several previous findings on MEAMs and autobiographical memory more generally, indicating that this naturalistic method was able to capture genuine MEAM experiences. Implications for future research on naturally occurring MEAMs are discussed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".