MétaCan
Menu
Back to cohort
Record W2135082687 · doi:10.3389/fnhum.2012.00026

Searching for Roots of Entrainment and Joint Action in Early Musical Interactions

2012· article· en· W2135082687 on OpenAlexaff
Jessica Phillips-Silver, Peter E. Keller

Bibliographic record

VenueFrontiers in Human Neuroscience · 2012
Typearticle
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsInternational Laboratory for Brain, Music and Sound Research
Fundersnot available
KeywordsEntrainment (biomusicology)MusicalAction (physics)ImitationPsychologyRhythmCognitive psychologyDanceJoint (building)CommunicationCognitive scienceComputer scienceAestheticsSocial psychologyArtVisual artsPhysics

Abstract

fetched live from OpenAlex

When people play music and dance together, they engage in forms of musical joint action that are often characterized by a shared sense of rhythmic timing and affective state (i.e., temporal and affective entrainment). In order to understand the origins of musical joint action, we propose a model in which entrainment is linked to dual mechanisms (motor resonance and action simulation), which in turn support musical behavior (imitation and complementary joint action). To illustrate this model, we consider two generic forms of joint musical behavior: chorusing and turn-taking. We explore how these common behaviors can be founded on entrainment capacities established early in human development, specifically during musical interactions between infants and their caregivers. If the roots of entrainment are found in early musical interactions which are practiced from childhood into adulthood, then we propose that the rehearsal of advanced musical ensemble skills can be considered to be a refined, mimetic form of temporal and affective entrainment whose evolution begins in infancy.

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)0.003
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.002
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0000.001
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 head0.120
GPT teacher head0.380
Teacher spread0.260 · 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

Citations198
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueFrontiers in Human NeuroscienceSame topicAction Observation and SynchronizationFrench-language works237,207