They’re (not) playing our song: (Ir)religious identity moderates the effects of listening to religious music on memory, self‐esteem, and mood
Bibliographic record
Abstract
Abstract Previous research suggests that listening to music can enhance memory and well‐being. However, what is often missing from this analysis is consideration of the social dimensions of music—for example, its capacity to affirm or threaten listeners’ social identities. This study examined whether (ir)religious music that was potentially identity‐affirming or identity‐threatening (Christian hymns, Buddhist chants, classical, or no music) would affect Christians’ and Atheists’ (N = 267) well‐being and memory performance while listening. Analyses revealed significant interactions between (ir)religious group and music type on memory, self‐esteem, and mood. Listening to music that potentially threatened one's religious identity appeared to undermine both performance self‐esteem and actual memory performance, while increasing feelings of hostility. This pattern was found for Christians (vs. Atheists) who listened to Buddhist chants. Conversely, Atheists’ performance self‐esteem (and to some degree their memory performance) was lowest, and their hostility highest, when they listened to Christian hymns. In this way, listening to music that potentially threatened one's religious group identity (or lack thereof) appeared to be detrimental for memory, self‐esteem, and mood. These results bridge research on the psychology of religion, music psychology, and social identity theorizing by demonstrating that the effects of music on memory and well‐being may reflect important (even sacred) social identities, with potential implications for individual well‐being and intergroup relations.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".