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Record W2913221039 · doi:10.1177/1747021819833968

Ears, heads, and eyes: When singers synchronise

2019· article· en· W2913221039 on OpenAlexafffund
Caroline Palmėr, Frances Spidle, Erik Koopmans, Peter Schubert

Bibliographic record

VenueQuarterly Journal of Experimental Psychology · 2019
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsPsychologyAudiologyCommunicationCognitive psychologyMedicine

Abstract

fetched live from OpenAlex

We examined the relationship between endogenous rhythms, auditory and visual cues, and body movement in the temporal coordination of duet singers. Sixteen pairs of experienced vocalists sang a familiar melody in Solo and two Duet conditions. Vocalists sang together in Unison (simultaneously producing identical pitches) and Round Duet conditions (one vocalist, the Follower, producing pitches at an eight-tone delay from their partner, the Leader) while facing Inward (full visual cues) and Outward (reduced visual cues). Larger tempo differences in partners' spontaneous (temporally unconstrained) Solo performances were associated with larger asynchrony in Duet performances, consistent with coupling predictions for oscillators with similar natural frequencies. Vocalists were slightly but consistently more synchronous in Duets when facing their partner (Inward) than when facing Outward; Unison and Round performances were equally synchronous. The greater difficulty of Rounds production was evidenced in vocalists' slower performance rates and more variable head movements; Followers directed their head gaze away from their partner and used bobbing head movements to mark the musical beat. The strength of Followers' head movements corresponded to the amount of tone onset asynchrony with their partners, indicating a strong association between timing and movement under increased attentional and working memory demands in music performance.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.128
Threshold uncertainty score0.505

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 communication0.0000.001
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 head0.031
GPT teacher head0.340
Teacher spread0.310 · 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

Citations40
Published2019
Admission routes2
Has abstractyes

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