Musical Preference: Role of Personality and Music-Related Acoustic Features
Bibliographic record
Abstract
Personality factors, typically determined by the Big Five Inventory (BFI), have been a primary method for investigating individual preferences in music. While these studies have yielded a number of insights into musical choices, weaknesses exist, owing to the methods by which music is characterized and categorized. For example, musical genre, music-preference dimensions (e.g., reflective and complex), and musical attributes (e.g., strong and mellow), reported within the literature, have arguably produced inconsistent and thus difficult to interpret results. We attempt to circumvent these inconsistencies by classifying music using objectively quantifiable acoustic features that are fundamental to Western music, such as tempo and register. Moreover, it is our contention that the link between musical preference and personality may operate primarily at the level of acoustic features and not at broader categorization levels, such as genre. This study attempts to address this issue. Ninety participants listened to and indicated preference for stimuli that were systematically manipulated by dynamics (attack rate), mode, register, and tempo. Personality was measured using the BFI, allowing for analysis of personality traits and preference for acoustic features. Results supported the link between personality and preference for certain acoustic features. Preference with respect to dynamics was related to openness and extraversion; mode to conscientiousness and extraversion; register to extraversion and neuroticism; and tempo to conscientiousness, extraversion, and neuroticism. Though significant, these associations were relatively weak; therefore, future research could expand the number of manipulated acoustic features. Specific attempts should also aim to disentangle the effects of genre versus acoustic features on musical preferences. Personality–preference relationships at the acoustic-feature level are discussed with respect to music recommender systems and other aspects of the literature.
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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 | 0.000 | 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) | 0.002 | 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".