What music makes us feel: At least 13 dimensions organize subjective experiences associated with music across different cultures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
= 1,258) participants listened to 2,168 music samples and reported on the specific feelings (e.g., "angry," "dreamy") or broad affective features (e.g., valence, arousal) that they made individuals feel. Using large-scale statistical tools, we uncovered 13 distinct types of subjective experience associated with music in both cultures. Specific feelings such as "triumphant" were better preserved across the 2 cultures than levels of valence and arousal, contrasting with theoretical claims that valence and arousal are building blocks of subjective experience. This held true even for music selected on the basis of its valence and arousal levels and for traditional Chinese music. Furthermore, the feelings associated with music were found to occupy continuous gradients, contradicting discrete emotion theories. Our findings, visualized within an interactive map (https://www.ocf.berkeley.edu/∼acowen/music.html) reveal a complex, high-dimensional space of subjective experience associated with music in multiple cultures. These findings can inform inquiries ranging from the etiology of affective disorders to the neurological basis of emotion.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it