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Record W4404393422 · doi:10.1525/mp.2024.42.2.109

The Effect of Aural and Visual Presentation Formats on Categorical and Dimensional Judgements of Emotion for Sung and Spoken Expressive Performances

2024· article· en· W4404393422 on OpenAlexaff
Peter Miksza, Daphne Tan, Robert F. Potter, McCall Booth

Bibliographic record

VenueMusic Perception An Interdisciplinary Journal · 2024
Typearticle
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCategorical variablePresentation (obstetrics)PsychologyCognitive psychologySpeech recognitionComputer scienceCommunicationNatural language processingLinguisticsMachine learning

Abstract

fetched live from OpenAlex

The purpose of this study was to investigate the effects of visual information on the perception of emotion in three contexts: a short spoken phrase, an analogous short melody, and a longer melody with greater complexity of pitch and rhythm. Participants without substantial formal music training were assigned to either an audio-only, visual-only, or audiovisual presentation mode; all observed musicians and actors who sang melodies or spoke phrases intending to communicate happiness, sadness, or anger. Participants rated these performances for positive and negative valence, energy arousal, and tension arousal. They were also asked to select the discrete emotion they perceived, and to rate how certain they were about this selection. Participants perceived energy arousal and tension arousal as fairly distinct features of the performances and the performances were perceived as having clear dimensional characteristics based on intended emotion (e.g., happy performances had the highest positive valence, lowest negative valence, etc.). Regarding presentation, participants in the audiovisual mode categorized emotional intentions with greater accuracy than those in the others. Although ratings of negative valence, energy, and tension ratings were more extreme for actors than for musicians, participants were most in agreement about the valence of the actors’ sung performances.

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)0.001
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 score0.834
Threshold uncertainty score0.363

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.407
Teacher spread0.377 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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