Explaining strong emotional responses to music: Sociality and intimacy.
Bibliographic record
Abstract
Why the keening sounds from Mississippi should strike notes of thrill and terror and wonder in hearts in the suburbs of London, I don't know.It can only be because it goes beyond color, blood -it goes to the bone. -Keith Richards The Problem: Why Does Music Evoke Strong Emotional Responses?Among the music most of us hear everyday, willingly or otherwise, in public spaces and private, some is tolerable, some merely pleasant, and some exasperating.Yet once in a while, or often if we are fortunate, we can hear music which inspires awe, transfixes us, even stops us in our tracks.Listeners have recounted being overwhelmed or overpowered by music, being reduced to tears, and experiencing chills or shivers and other bodily sensations.These kinds of experiences will be the focus of this paper.I address the following questions: How can music, understood as an art of sound, prompt powerful physical and emotional responses in listeners?To what extent can we attribute such responses to the sonic qualities of music, to psychological and neurological factors, or to cultural factors?Can these responses tell us anything about music or about ourselves?To anticipate my conclusions: Certain structural features of music seem to have direct physical effects on listeners.Quite general claims can be made about the type of music which provokes these responses,
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".