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Record W2145169994 · doi:10.18061/1811/36606

Extracting the Beat: An Experience-dependent Complex Integration of Multisensory Information Involving Multiple Levels of the Nervous System

2009· article· en· W2145169994 on OpenAlexafffund
Laurel J. Trainor, Andrea Unrau

Bibliographic record

VenueEmpirical Musicology Review · 2009
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRhythmVestibular systemPerceptionBeat (acoustics)Stimulus modalityMultisensory integrationNeurosciencePsychologySensory systemAuditory perceptionCognitive psychologyCommunicationMedicine

Abstract

fetched live from OpenAlex

In a series of studies we have shown that movement (or vestibular stimulation) that is synchronized to every second or every third beat of a metrically ambiguous rhythm pattern biases people to perceive the meter as a march or as a waltz, respectively. Riggle (this volume) claims that we postulate an "innate", "specialized brain unit" for beat perception that is "directly" influenced by vestibular input. In fact, to the contrary, we argue that experience likely plays a large role in the development of rhythmic auditory-movement interactions, and that rhythmic processing in the brain is widely distributed and includes subcortical and cortical areas involved in sound processing and movement. Further, we argue that vestibular and auditory information are integrated at various subcortical and cortical levels along with input from other sensory modalities, and it is not clear which levels are most important for rhythm processing or, indeed, what a "direct" influence of vestibular input would mean. Finally, we argue that vestibular input to sound location mechanisms may be involved, but likely cannot explain the influence of vestibular input on the perception of auditory rhythm. This remains an empirical question for future research.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.819
Threshold uncertainty score0.344

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.248
GPT teacher head0.388
Teacher spread0.140 · 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 designBench or experimental
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

Citations13
Published2009
Admission routes2
Has abstractyes

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