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Record W2021370281 · doi:10.1525/mp.2011.29.2.185

Music Lessons, Emotional Intelligence, and IQ

2011· article· en· W2021370281 on OpenAlexaff
E. Glenn Schellenberg

Bibliographic record

VenueMusic Perception An Interdisciplinary Journal · 2011
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEmotional intelligencePsychologyNonverbal communicationTest (biology)Intelligence quotientDevelopmental psychologyVerbal reasoningCognitive psychologyCognition

Abstract

fetched live from OpenAlex

musically trained and untrained participants were administered tests of emotional intelligence and IQ. As in previous research, trained participants scored higher than untrained participants on the IQ Composite score and on its Verbal and Nonverbal subtests. The advantage for the trained group on the Composite score and on the Nonverbal subtest was evident even when gender, parents' education, family income, and first language were held constant. The groups performed similarly, however, on the test of emotional intelligence, and scores on the IQ test were only weakly correlated with scores on the emotional intelligence test. The results imply that (1) associations between music lessons and nonmusical abilities are limited to intellectual abilities, and/or (2) associations between music lessons and emotional intelligence are not evident on visual- and/or text-based tests of emotional intelligence such as the one used here.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.188
GPT teacher head0.398
Teacher spread0.210 · 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 source (direct Gemma or distilled Codex), 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

Citations108
Published2011
Admission routes1
Has abstractyes

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