Effects of alexithymia and its subfactors on emotional response to music
Bibliographic record
Abstract
Alexithymia is a trait characterized by decreased emotional response to visual or verbal stimuli. However, Lyvers et al. suggest that alexithymia is positively correlated with the magnitude of emotional response to music (ERM). This study reexamines their findings in two sets of Asian population (China [ n = 344] and Japan [ n = 341]) using online surveys to investigate the subfactors of alexithymia. The Toronto Alexithymia Scale and the Geneva Emotional Music Scale (GEMS) were used to measure alexithymia and ERM in the participants, along with trait anxiety and music experiences in their daily lives. The obtained data showed that in both the Chinese and the Japanese populations, individuals with higher alexithymia tend to have higher GEMS scores (i.e., higher ERM), which is consistent with the finding of the previous study. Furthermore, alexithymia and ERM are correlated because of difficulty in identifying feelings, a subfactor of alexithymia. These results were not influenced by trait anxiety. In addition, alexithymia showed no correlation with either the frequency of listening to music or the degree of music absorption in daily life. We argued that the nonverbal characteristic of music might be key to the association between alexithymia and ERM.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".