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Music Training and Cognitive Abilities: Associations, Causes, and Consequences

2018· reference-entry· en· W2896428155 on OpenAlexafffund
Swathi Swaminathan, E. Glenn Schellenberg

Bibliographic record

Venuenot available
Typereference-entry
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsUniversity of TorontoBaycrest Hospital
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPsychologyActive listeningCognitive psychologyCognitionAptitudeReading (process)PersonalityPerceptionDevelopmental psychologyDyslexiaCognitive skillMusic perceptionMusic psychologyRhythmMusic educationSocial psychologyCommunicationLinguisticsMedicine

Abstract

fetched live from OpenAlex

This chapter evaluates the evidence that music training leads to improved cognitive abilities. It considers whether music training is associated with measures of general cognitive abilities, visuospatial abilities, and language abilities, as well as with real-world measures such as academic achievement and healthy aging. Although positive associations with music training are evident in each instance, causal evidence is lacking, inconsistent, or weak. The one exception appears to be specialized music training that focuses on listening skills and rhythm perception, which seems to improve listening skills more generally. Improved phonological awareness can, in turn, lead to improvements in reading, particularly for young children who are beginning to read, or for children with dyslexia. Otherwise, associations with music training appear to be the consequence of individual differences in demographics, personality, music aptitude, and cognitive ability, which influence who takes music lessons, particularly for extended durations of time.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.508
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.141
GPT teacher head0.327
Teacher spread0.186 · 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.

Study designNot applicable
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

Citations54
Published2018
Admission routes2
Has abstractyes

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