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Record W2009774843 · doi:10.1080/13554794.2014.960428

Supplementary motor area and primary auditory cortex activation in an expert break-dancer during the kinesthetic motor imagery of dance to music

2014· article· en· W2009774843 on OpenAlexafffund
Michael P. Olshansky, Rachel J. Bar, Mary Fogarty, Joseph F. X. DeSouza

Bibliographic record

VenueNeurocase · 2014
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsCanadians Living with HIVToronto Metropolitan UniversityYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsKinesthetic learningDancePsychologyAuditory imagerySupplementary motor areaFunctional magnetic resonance imagingSMA*Primary motor cortexMotor imageryCognitive psychologyAuditory cortexMotor cortexNeuroscienceAudiologyMental imageDevelopmental psychologyVisual artsElectroencephalographyCognitionArtComputer scienceMedicine

Abstract

fetched live from OpenAlex

The current study used functional magnetic resonance imaging to examine the neural activity of an expert dancer with 35 years of break-dancing experience during the kinesthetic motor imagery (KMI) of dance accompanied by highly familiar and unfamiliar music. The goal of this study was to examine the effect of musical familiarity on neural activity underlying KMI within a highly experienced dancer. In order to investigate this in both primary sensory and motor planning cortical areas, we examined the effects of music familiarity on the primary auditory cortex [Heschl's gyrus (HG)] and the supplementary motor area (SMA). Our findings reveal reduced HG activity and greater SMA activity during imagined dance to familiar music compared to unfamiliar music. We propose that one's internal representations of dance moves are influenced by auditory stimuli and may be specific to a dance style and the music accompanying it.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.024
GPT teacher head0.261
Teacher spread0.237 · 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

Citations39
Published2014
Admission routes2
Has abstractyes

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