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Record W3195860249 · doi:10.1177/03057356211030985

Undergraduate students with musical training report less conflict in interpersonal relationships

2021· article· en· W3195860249 on OpenAlexaff
Jordan MacDonald, Jonathan M. P. Wilbiks

Bibliographic record

VenuePsychology of Music · 2021
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsPsychologyInterpersonal communicationMusicalPerceptionProsodyQuality (philosophy)Interpersonal relationshipCognitive psychologyCognitionSocial psychologyApplied psychologyLinguistics

Abstract

fetched live from OpenAlex

Recent research has shown that formal musical training has a wealth of benefits in terms of cognition, mental health, social skills, and even speech perception. Of these benefits, there is strong support for a relationship between formal musical training and an improved ability to recognize emotions in speech prosody. Given this connection, interpersonal relationships stand to benefit from improved communication efficacy, which includes an improved ability to recognize emotions in speech. Interpersonal relationships rely on successful expression and interpretation of emotions in speech. If formal musical training can improve the perception of emotions in speech, it should indirectly benefit interpersonal relationship quality. The current study collected data from 197 undergraduate students about their formal musical training and interpersonal relationship quality through an online survey. The results showed that formal musical training accounted for 8% of the difference in relationship conflict but did not benefit relationship support or depth. While musical expertise does not necessarily improve relationship quality overall, it may help reduce conflict in relationships. Further research is needed, with participants who have greater musical expertise, to clarify the relationship between formal musical training and relationship conflict.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.947
Threshold uncertainty score0.457

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.258
GPT teacher head0.395
Teacher spread0.137 · 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 designObservational
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

Citations4
Published2021
Admission routes1
Has abstractyes

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