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Record W2974036807 · doi:10.1525/mp.2019.37.1.66

Acoustically Expressing Affect

2019· article· en· W2974036807 on OpenAlexaff
Aimee Battcock, Michael Schutz

Bibliographic record

VenueMusic Perception An Interdisciplinary Journal · 2019
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPsychologyValence (chemistry)ArousalActive listeningInterpersonal communicationAffect (linguistics)Cognitive psychologySocial psychologySpeech recognitionCommunicationComputer sciencePhysics

Abstract

fetched live from OpenAlex

Composers convey emotion through music by co-varying structural cues. Although the complex interplay provides a rich listening experience, this creates challenges for understanding the contributions of individual cues. Here we investigate how three specific cues (attack rate, mode, and pitch height) work together to convey emotion in Bach's Well Tempered-Clavier (WTC). In three experiments, we explore responses to (1) eight-measure excerpts and (2) musically “resolved” excerpts, and (3) investigate the role of different standard dimensional scales of emotion. In each experiment, thirty nonmusician participants rated perceived emotion along scales of valence and intensity (Experiments 1 & 2) or valence and arousal (Experiment 3) for 48 pieces in the WTC. Responses indicate listeners used attack rate, Mode, and pitch height to make judgements of valence, but only attack rate for intensity/arousal. Commonality analyses revealed mode predicted the most variance for valence ratings, followed by attack rate, with pitch height contributing minimally. In Experiment 2 mode increased in predictive power compared to Experiment 1. For Experiment 3, using “arousal” instead of “intensity” showed similar results to Experiment 1. We discuss how these results complement and extend previous findings of studies with tightly controlled stimuli, providing additional perspective on complex issues of interpersonal communication.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.959
Threshold uncertainty score0.999

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 studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.002

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 head0.050
GPT teacher head0.349
Teacher spread0.298 · 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; both teacher heads agree on what is shown here.

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

Citations19
Published2019
Admission routes1
Has abstractyes

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