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Record W2053261569 · doi:10.3389/fpsyg.2014.01257

Neural correlates of intentional switching from ternary to binary meter in a musical hemiola pattern

2014· article· en· W2053261569 on OpenAlexafffund
Takako Fujioka, Brian C. Fidali, Bernhard Roß

Bibliographic record

VenueFrontiers in Psychology · 2014
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsUniversity of TorontoBaycrest Hospital
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPsychologyContext (archaeology)MetronomeRhythmPremotor cortexNeuroscienceCognitive psychologyCommunicationAcoustics

Abstract

fetched live from OpenAlex

Musical rhythms are often perceived and interpreted within a metrical framework that integrates timing information hierarchically based on interval ratios. Endogenous timing processes facilitate this metrical integration and allow us using the sensory context for predicting when an expected sensory event will happen ("predictive timing"). Previously, we showed that listening to metronomes and subjectively imagining the two different meters of march and waltz modulated the resulting auditory evoked responses in the temporal lobe and motor-related brain areas such as the motor cortex, basal ganglia, and cerebellum. Here we further explored the intentional transitions between the two metrical contexts, known as hemiola in the Western classical music dating back to the sixteenth century. We examined MEG from 12 musicians while they repeatedly listened to a sequence of 12 unaccented clicks with an interval of 390 ms, and tapped to them with the right hand according to a 3 + 3 + 2 + 2 + 2 hemiola accent pattern. While participants listened to the same metronome sequence and imagined the accents, their pattern of brain responses significantly changed just before the "pivot" point of metric transition from ternary to binary meter. Until 100 ms before the pivot point, brain activities were more similar to those in the simple ternary meter than those in the simple binary meter, but the pattern was reversed afterwards. A similar transition was also observed at the downbeat after the pivot. Brain areas related to the metric transition were identified from source reconstruction of the MEG using a beamformer and included auditory cortices, sensorimotor and premotor cortices, cerebellum, inferior/middle frontal gyrus, parahippocampal gyrus, inferior parietal lobule, cingulate cortex, and precuneus. The results strongly support that predictive timing processes related to auditory-motor, fronto-parietal, and medial limbic systems underlie metrical representation and its transitions.

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 score0.001
Threshold uncertainty score0.004

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)0.0010.000

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 head0.031
GPT teacher head0.313
Teacher spread0.282 · 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

Citations17
Published2014
Admission routes2
Has abstractyes

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