Music-evoked autobiographical memories, emotion regulation, time perspective, and mental health
Bibliographic record
Abstract
The intriguing phenomenon by which songs trigger the recall of self-defining moments in one’s past is called music-evoked autobiographical memories (MEAMs). This study examines if various types of MEAMs can partially mediate the impact that emotion regulation (reappraisal and suppression) and time perspective (past positive and past negative) can have on mental health (internalizing symptoms and happiness) in youth. To this end, we developed the Music Evoked Memory Orientation Scale (MEMOS), which was specifically designed to assess the phenomenological characteristics of MEAMs. The sample consisted of 397 undergraduate students (M age = 19.17 years; 80.6% female) that attended a Canadian university. Results revealed that the MEMOS had adequate psychometric qualities in terms of factorial validity, internal consistency, convergent validity, and incremental validity. Most notably, analyses indicated that social sharing of MEAMs mediated the relationships between emotion regulation (reappraisal and suppression) and happiness. However, aspects of MEAMs (self-identification, social sharing, and coherence) did not mediate any relationship between emotion regulation and internalizing symptoms. Also, of particular interest, social sharing of and coherence in MEAMs mediated the relationships between past positive and mental health. However, aspects of MEAMs (self-identification, social sharing, and coherence) did not mediate relationships between past negative and mental health. Interestingly, mediated effects from MEAMs held despite controlling for personality traits (Extraversion and Emotional Stability). In sum, future studies need to examine why only some MEAMs, particularly the social sharing of MEAMs, matter at the intersection of emotion regulation, time perspective, and mental health in youth.
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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 | 0.000 |
| Scholarly communication | 0.001 | 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".