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Record W4253674829 · doi:10.31234/osf.io/9yrkv

Rhythm in dyadic interactions

2020· preprint· en· W4253674829 on OpenAlexaff
Koen de Reus, Masayo Soma, Mariana Anichini, Marco Gamba, Marianne de Heer Kloots, Miriam Lense, Julia Hyland Bruno, Laurel J. Trainor, Andrea Ravignani

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Vocal Communication and Behavior
Canadian institutionsMcMaster University
Fundersnot available
KeywordsRhythmFocus (optics)Cognitive psychologyMultidisciplinary approachPrimatePsychologyModality (human–computer interaction)CommunicationBiologyNeuroscienceComputer scienceHuman–computer interactionSociology

Abstract

fetched live from OpenAlex

This review paper discusses rhythmic interactions and distinguishes them from non-rhythmic interactions. We report on communicative behaviours in social and sexual contexts, as found in dyads of humans, non-human primates, non-primate mammals, birds, anurans and insects. We discuss observed instances of rhythm in dyadic interactions, identify knowledge gaps and propose suggestions for future research. We find that most studies on rhythmicity in interactive signals mainly focus on one modality (acoustic or visual) and suggest more work should be performed on multimodal signals. Although the social functions of interactive rhythms have been fairly well described, developmental research on rhythms used to regulate social interactions is still lacking. Future work should also focus on identifying the exact timing mechanisms involved. Rhythmic signalling behaviours are widespread and critical in regulating social interactions across taxa, but many questions remain unexplored. A multidisciplinary, comparative cross-species approach may help provide answers.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.790
Threshold uncertainty score0.463

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 science0.0000.001
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 head0.064
GPT teacher head0.351
Teacher spread0.287 · 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 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

Citations5
Published2020
Admission routes1
Has abstractyes

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