Musician Advantages in Music Perception: An Issue of Motivation, Not Just Ability
Bibliographic record
Abstract
two experiments examined effects of regulatory fit and music training on performance on one subtest of the Montreal Battery of Evaluation of Amusia (MBEA). Participants made same-different judgments about melody pairs, while either gaining points for correct answers (gains condition) or losing points for incorrect answers (losses condition). In Experiment 1, participants were told that the test was diagnostic of their music ability and then were asked to self-identify as a musician or a nonmusician. In Experiment 2, participants were given either a promotion-focus prime (a performance-based opportunity to gain entry into a raffle) or a prevention-focus prime (a raffle ticket was awarded at the start of the experiment and participants prevented its loss by maintaining a certain level of performance). Consistent with a regulatory fit hypothesis, nonmusicians and promotion-primed participants performed better in the gains condition than the losses condition, while musicians and prevention-primed participants performed better in the losses condition than the gains condition. Experiment 2 additionally revealed that regulatory fit effects were stronger for musicians than nonmusicians. This study demonstrates that regulatory fit impacts performance on the MBEA and highlights the importance of motivational orientation with respect to musician performance advantages in music perception.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".