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Record W2160838747 · doi:10.1525/mp.2009.26.5.475

Facial Expressions and Emotional Singing: A Study of Perception and Production with Motion Capture and Electromyography

2009· article· en· W2160838747 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMusic Perception An Interdisciplinary Journal · 2009
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsToronto Metropolitan UniversityMcGill University
Fundersnot available
KeywordsPsychologyImitationFacial expressionSingingEmotional expressionPerceptionStimulus (psychology)Facial electromyographyCognitive psychologyCommunicationElectromyographySpeech recognitionSocial psychologyComputer scienceAcousticsNeuroscience

Abstract

fetched live from OpenAlex

FACIAL EXPRESSIONS ARE USED IN MUSIC PERFORMANCE to communicate structural and emotional intentions. Exposure to emotional facial expressions also may lead to subtle facial movements that mirror those expressions. Seven participants were recorded with motion capture as they watched and imitated phrases of emotional singing. Four different participants were recorded using facial electromyography (EMG) while performing the same task. Participants saw and heard recordings of musical phrases sung with happy, sad, and neutral emotional connotations. They then imitated the target stimulus, paying close attention to the emotion expressed. Facial expressions were monitored during four epochs: (a) during the target; (b) prior to their imitation; (c) during their imitation; and (d) after their imitation. Expressive activity was observed in all epochs, implicating a role of facial expressions in the perception, planning, production, and post-production of emotional singing.

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.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.967
Threshold uncertainty score0.955

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 studies0.0010.000
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 head0.030
GPT teacher head0.306
Teacher spread0.276 · 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