MétaCan
Menu
Back to cohort
Record W4414453670 · doi:10.1093/pnasnexus/pgaf306

Body maps of the sensation of musical groove

2025· article· en· W4414453670 on OpenAlexafffund
Maria A. G. Witek, Tomas E. Matthews, Toni Bechtold, Virginia B. Penhune

Bibliographic record

VenuePNAS Nexus · 2025
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of CanadaDanmarks Grundforskningsfond
KeywordsEmbodied cognitionSensationPleasureFeelingMusicalRhythmMovement (music)Dance

Abstract

fetched live from OpenAlex

Rhythmic music often leads to an urge to move the body in time with the music. This urge to move can be a pleasurable experience. In psychology, we define the pleasurable wanting to move to music as groove. Here, we investigate where in the body these two groove components-movement and pleasure-are felt and whether the embodied sensations depend on the musical genre. Using a body sensation map paradigm, we found that the funk genre, which elicited high levels of groove, increased sensations across the whole body, including in the head, shoulders, upper chest, abdomen, arms, hands, hips, legs, and feet. Importantly, wanting to move and pleasure produced distinct body maps, with wanting to move associated with more sensation in the extremities and pleasure more associated with feelings in the chest and abdomen. Exploratory analyses also found an inverted U-shaped relationship between wanting to move and pleasure ratings and the rhythmic complexity of the excerpts, as indexed by pulse entropy, and that medium pulse entropy produced sensations in the upper chest, shoulders, hips, and ankles. The results are discussed in relation to theories of embodied predictive processing, highlighting the potential role of interoception in musical prediction and reward. Overall, our study shows clear patterns of embodied differentiation for different components and levels of groove.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.287
Threshold uncertainty score0.114

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)0.0000.000

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 head0.009
GPT teacher head0.232
Teacher spread0.223 · 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 designTheoretical or conceptual
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

Citations3
Published2025
Admission routes2
Has abstractyes

Explore more

Same venuePNAS NexusSame topicMusic Technology and Sound StudiesFrench-language works237,207