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Record W2777338366 · doi:10.1037/emo0000410

Generalizing Duchenne to sad expressions with binocular rivalry and perception ratings.

2018· article· en· W2777338366 on OpenAlexafffund
Nour Malek, Daniel S. Messinger, Andy Yuan Lee Gao, Eva G. Krumhuber, Whitney I. Mattson, Ridha Joober, Karim Tabbane, Julio Martínez-Trujillo

Bibliographic record

VenueEmotion · 2018
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsDouglas Mental Health University InstituteDouglas CollegeMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsPsychologyFacial expressionFace perceptionBinocular rivalryPerceptionCognitive psychologyPsycINFOEmotional expressionSocial psychologySocial perceptionDevelopmental psychologyVisual perceptionCommunication

Abstract

fetched live from OpenAlex

Discrete emotion theories emphasize the modularity of facial expressions, while functionalist theories suggest that a single facial action may have a common meaning across expressions. Smiles involving the Duchenne marker, eye constriction causing crow's feet, are perceived as intensely positive and sincere. To test whether the Duchenne marker is a general index of intensity and sincerity, we contrasted positive and negative expressions with and without the Duchenne marker in a binocular rivalry paradigm. Both smiles and sad expressions involving the Duchenne marker were perceived longer than non-Duchenne expressions, and participants rated all Duchenne expressions as more affectively intense and more sincere than their non-Duchenne counterparts. Correlations between perceptual dominance and ratings suggested that the Duchenne marker increased the dominance of smiles and sad expressions by increasing their perceived affective intensity. The results provide evidence in favor of Darwin's hypothesis that specific facial actions have a general function (conveying affect intensification and sincerity) across expressions. (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 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 score0.187
Threshold uncertainty score0.682

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.044
GPT teacher head0.285
Teacher spread0.241 · 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

Citations29
Published2018
Admission routes2
Has abstractyes

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