The relationship between musical training and musical empathizing and systemizing traits
Bibliographic record
Abstract
Although individual differences in engagement with and response to music are well documented, little is known about variations in musical empathizing and systemizing (E-S) traits and their relation to musical sophistication, including musical training. The current study examines the relationship between musical and general (non-musical) E-S traits and how musical sophistication and specific aspects of musical training are related to musical E-S traits. A total of 81 respondents reported on their level of musical sophistication and training (e.g., musical abilities, formal training, and engagement in musical activities) and endorsement of musical and general E-S traits. Participants were asked to complete the Goldsmiths Musical Sophistication Index (musical sophistication and training), the Empathizing and Systemizing quotients (general, non-musical E-S traits), and the Musical Empathizing and Systemizing inventory (musical E-S traits). Results suggest that general E-S traits are related to musical E-S traits and that musical sophistication, including but not limited to formal training, is positively associated with musical E-S traits. Furthermore, greater music training, as measured by the number of instruments played and years of formal instrumental and theory training, is related to greater endorsement of E-S traits. This study provides grounds for assessing the link between musical sophistication and training and musical E-S traits within clinical populations that have atypicalities in empathizing (e.g., autism spectrum disorder).
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".