Individual differences in autism traits, personality, and emotional responsiveness to music in the general population
Bibliographic record
Abstract
Little is known about the relationship between the personality and the emotional experiences of people with broader autism phenotype (BAP) or autistic traits. Given that music is a powerful vehicle for conveying emotions and that several studies show that people with and without autism spectrum disorder (ASD) typically respond to music in similar ways, the present study examines the relationship between personality, autistic traits, and emotional experiences evoked by music. A total of 110 participants ( n = 74 females) aged 18 to 35 years ( M = 21.25, SD = 3.36) completed the NEO-Five Factor Inventory-3-S, Social Responsiveness Scale-2, Autism-Spectrum Quotient, and Goldsmiths Musical Sophistication Index self-report questionnaires. Path analyses revealed that the relationship between autistic traits and emotional responsiveness to music was fully mediated by extraversion, and to a lesser extent openness to experience. These results suggest that people in the general population who have fewer autistic traits and who tend to be extraverted and open to experience report greater emotional responsiveness to music than those who are less extraverted and less open to experience. These findings suggest that it is important to consider personality characteristics when considering the relationship between autistic traits and emotional experiences.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".