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Record W2299661643 · doi:10.14288/1.0076815

The effects of music playing on cognitive task performance

2011· article· en· W2299661643 on OpenAlexaff
Sabrina Chang

Bibliographic record

VenuecIRcle (University of British Columbia) · 2011
Typearticle
Languageen
FieldPsychology
TopicColor perception and design
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsTask (project management)CognitionCognitive psychologyPsychologyComputer scienceEngineering

Abstract

fetched live from OpenAlex

Numerous music cognition studies have demonstrated the cognitive benefits of both long-term and short-term musical training. Whereas a great number of these studies deal with the short-term benefits for the music listener or the longer term benefits for the novice or accomplished musician, our study examines the short-term effects of music playing for the advanced performer. For our pretest-posttest design, we recruited advanced classically/score-based trained pianists. The participants started by completing a creative exercise (alternative uses task) or detail-oriented exercise (proofreading task). They then performed a piano piece for ten minutes. The performances were followed by completion of the second cognitive task (whichever task they were not given in the pretest condition). No significant pretest-posttest differences in creativity were reported. However, we found that participants performed significantly worse in the posttest detail-oriented task. Our results suggest that performance in tasks involving attention to detail—specifically, a proofreading task involving the visual detection of errors — may be hindered immediately following a short period of score-based music playing when the piece is already familiar to the performer.

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 score0.973
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.203
Teacher spread0.182 · 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

Citations0
Published2011
Admission routes1
Has abstractyes

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