Some like it sharp: Song familiarity influences musical preference for absolute tuning.
Bibliographic record
Abstract
Listening to music is an enjoyable activity for most individuals, yet the factors that relate to aesthetic preferences are not completely understood.In the present paper, we investigate whether the absolute tuning of music influences listener evaluations of music.Across three experiments, participants rated musical excerpts, tuned conventionally (A4 = 440 Hz) versus unconventionally (± 50 cents from conventional tuning), in terms of aesthetic preference.In Experiment 1, participants rated single MIDI piano excerpts on each trial in terms of liking, interest, and unusualness.In Experiments 2 and 3, participants heard two versions of the same excerpt on each trial, only differing in terms of tuning, and made a forced-choice judgment as to which version they preferred.Experiment 2 used the same piano excerpts as Experiment 1, whereas Experiment 3 introduced both highly familiar and unknown song excerpts by professional recording artists.Overall, the results suggest that absolute tuning influences aesthetic preferences under limited circumstances.Although there was no strong evidence for tuning influencing judgments in either Experiments 1 or 2, we found a robust effect in Experiment 3 depending on the familiarity of the recording.Whereas participants clearly preferred the conventionally tuned version for highly familiar recordings, they tended to prefer the version that was highest in absolute pitch if the recording was unfamiliar.Overall, these results suggest that absolute tuning can influence musical preferences, although the specific nature of the effect depends on familiarity.
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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 | 0.006 |
| 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) | 0.005 | 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".