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Record W2946413308 · doi:10.1037/xge0000624

Acting in action: Prosodic analysis of character portrayal during acting.

2019· article· en· W2946413308 on OpenAlexafffund
Matthew Berry, Steven Brown

Bibliographic record

VenueJournal of Experimental Psychology General · 2019
Typearticle
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCooperativenessPsychologyAssertivenessTimbreCharacter (mathematics)LoudnessAction (physics)UtteranceBig Five personality traitsSocial psychologyDuration (music)LinguisticsPersonalityCognitive psychologyArtComputer scienceLiteratureMusical

Abstract

fetched live from OpenAlex

During the process of acting, actors have to embody the characters that they are portraying by changing their vocal and gestural features to match standard conceptions of the characters. In this experimental study of acting, we had professional actors portray a series of stock characters (e.g., king, bully, lover), which were organized according to a predictive scheme based on the 2 orthogonal personality dimensions of assertiveness and cooperativeness. We measured 12 prosodic features of the actors' vocal productions, as related to pitch, loudness, timbre, and duration/timing. The results showed a significant effect of character assertiveness on all 12 vocal parameters, but a weaker effect of cooperativeness on fewer vocal parameters. These findings comprise the first experimental analysis of vocal gesturing during character portrayal in actors and demonstrate that actors reliably manipulate prosodic cues in a contrastive manner to differentiate characters based on their personality traits. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.801
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0140.000

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 head0.074
GPT teacher head0.439
Teacher spread0.365 · 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.

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

Citations18
Published2019
Admission routes2
Has abstractyes

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