MétaCan
Menu
Back to cohort
Record W4281715241 · doi:10.1177/03057356221098096

Effects of alexithymia and its subfactors on emotional response to music

2022· article· en· W4281715241 on OpenAlexaboutno aff
Jingni Liu, Hirokata Fukushima

Bibliographic record

VenuePsychology of Music · 2022
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsnot available
FundersJapan Society for the Promotion of Science
KeywordsAlexithymiaPsychologyToronto Alexithymia ScaleAssociation (psychology)AnxietyFeelingTraitPopulationDevelopmental psychologyClinical psychologySocial psychologyPsychotherapistPsychiatryMedicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.721
Threshold uncertainty score0.477

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.057
GPT teacher head0.323
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venuePsychology of MusicSame topicNeuroscience and Music PerceptionFrench-language works237,207