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Record W2766923222 · doi:10.3389/fphys.2017.00785

Increase in Synchronization of Autonomic Rhythms between Individuals When Listening to Music

2017· article· en· W2766923222 on OpenAlexaff
Nicolò F. Bernardi, Erwan Codrons, Rita di Leo, Matteo Vandoni, Filippo Cavallaro, Giuseppe Vita, Luciano Bernardi

Bibliographic record

VenueFrontiers in Physiology · 2017
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsInternational Laboratory for Brain, Music and Sound ResearchMcGill University
FundersUniversità degli Studi di PaviaSigne ja Ane Gyllenbergin Säätiö
KeywordsRhythmMelodyActive listeningPsychologyCognitive psychologyMusicalConsonance and dissonanceImitationDuration (music)CommunicationAudiologyNeuroscienceAcousticsMedicineAestheticsArtVisual arts

Abstract

fetched live from OpenAlex

In light of theories postulating a role for music in forming emotional and social bonds, here we investigated whether endogenous rhythms synchronize between multiple individuals when listening to music. Cardiovascular and respiratory recordings were taken from multiple individuals (musically trained or music-naïve) simultaneously, at rest and during a live concert comprising music excerpts with varying degrees of complexity of the acoustic envelope. Inter-individual synchronization of cardiorespiratory rhythms showed a subtle but reliable increase during passively listening to music compared to baseline. The low-level auditory features of the music were largely responsible for creating or disrupting such synchronism, explaining ~80% of its variance, over and beyond subjective musical preferences and previous musical training. Listening to simple rhythms and melodies, which largely dominate the choice of music during rituals and mass events, brings individuals together in terms of their physiological rhythms, which could explain why music is widely used to favor social bonds.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.885
Threshold uncertainty score0.500

Codex and Gemma teacher scores by category

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 science0.0010.000
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 head0.034
GPT teacher head0.282
Teacher spread0.248 · 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 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

Citations62
Published2017
Admission routes1
Has abstractyes

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