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Record W2293759008 · doi:10.1111/infa.12140

Social Effects of Movement Synchrony: Increased Infant Helpfulness only Transfers to Affiliates of Synchronously Moving Partners

2016· article· en· W2293759008 on OpenAlexaff
Laura K. Cirelli, Stephanie Wan, Laurel J. Trainor

Bibliographic record

VenueInfancy · 2016
Typearticle
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsBaycrest HospitalMcMaster University
Fundersnot available
KeywordsHelpfulnessPsychologyInterpersonal communicationMovement (music)Developmental psychologyAsynchronous communicationSocial psychologySocial relationInterpersonal relationshipCommunicationComputer science

Abstract

fetched live from OpenAlex

Interpersonal synchrony increases cooperation among adults, children, and infants. We tested whether increased infant helpfulness transfers to individuals uninvolved in the movement, but shown to be affiliates of a synchronously moving partner. Initially, 14‐month‐old infants (N = 48) watched a live skit by Experimenters 1 and 2 that either demonstrated affiliation or individuality. Infants in both groups were then randomly assigned to be bounced to music either synchronously or asynchronously with Experimenter 1. Infant instrumental helpfulness toward Experimenter 2 was then measured. If the two experimenters were affiliates, infants from the synchronous movement condition were significantly more helpful toward Experimenter 2 than infants from the asynchronous movement condition. However, if the two experimenters were not affiliated, synchrony effects on prosociality did not transfer to Experimenter 2. These results show the importance of musical synchrony for social interaction and suggest that infants may use an understanding of third‐party social relationships when directing their own social behaviors.

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)0.001
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 science0.0000.001
Research integrity0.0000.000
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 head0.016
GPT teacher head0.312
Teacher spread0.296 · 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

Citations148
Published2016
Admission routes1
Has abstractyes

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