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Record W4409499267 · doi:10.1525/mp.2025.2427447

Marching to Your Own Beat

2025· article· en· W4409499267 on OpenAlexaff
Jonathan Cannon

Bibliographic record

VenueMusic Perception An Interdisciplinary Journal · 2025
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsMcMaster University
Fundersnot available
KeywordsRhythmBeat (acoustics)Entrainment (biomusicology)Synchronization (alternating current)PerceptionLink (geometry)Computer scienceSpeech recognitionPsychologyCommunicationArtPhysicsTelecommunicationsComputer networkNeuroscienceAcousticsAestheticsChannel (broadcasting)

Abstract

fetched live from OpenAlex

In the study of auditory rhythm perception, a key question is the relationship between the perception of a beat and moving to the beat. The most obvious way to observe an individual’s perceived beat is to ask them to move along with it (e.g., tap a finger on the beat). But this type of observation affects the observed percept: experiments show that rhythm perception is altered in several ways during movement, even if that movement is as minimal as a finger tap. In particular, rhythmic movement seems to give perceptual “momentum” to a beat percept, helping it continue and making it robust to input perturbations and complexity. Here, we argue that this phenomenon can be elegantly accounted for by assuming that our perception of a periodic beat is partly entrained by the sensory feedback from our own movement. We show that this under-studied aspect of human rhythm provides a parsimonious explanation for a range of experimental results in healthy and disordered populations; that it poses challenges to existing models of rhythm production and entrainment; and that it may be a key to understanding the role of the cerebellum in sensorimotor synchronization and the observation of synchronization differences in autism.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
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 score0.888
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.065
GPT teacher head0.386
Teacher spread0.321 · 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 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

Citations1
Published2025
Admission routes1
Has abstractyes

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