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Record W4376642979 · doi:10.1080/02699931.2023.2212892

Exploration of visual factors in the disgust-anger confusion: the importance of the mouth

2023· article· en· W4376642979 on OpenAlexafffund
Emalie Hendel, Adèle Gallant, Marie-Pier Mazerolle, Sabah-Izayah Cyr, Annie Roy‐Charland

Bibliographic record

VenueCognition & Emotion · 2023
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsUniversité de Moncton
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsDisgustAngerPsychologyPerceptionCognitive psychologyConfusionFixation (population genetics)Facial expressionSocial psychologyCommunicationPopulation

Abstract

fetched live from OpenAlex

According to the perceptual-attentional limitations hypothesis, the confusion between expressions of disgust and anger may be due to the difficulty in perceptually distinguishing the two, or insufficient attention to their distinctive cues. The objective of the current study was to test this hypothesis as an explanation for the confusion between expressions of disgust and anger in adults using eye-movements. In Experiment 1, participants were asked to identify each emotion in 96 trials composed of prototypes of anger and prototypes of disgust. In Experiment 2, fixation points oriented participants' attention toward the eyes, the nose, or the mouth of each prototype. Results revealed that disgust was less accurately recognised than anger (Experiment 1 and 2), especially when the mouth was open (Experiment 1 and 2), and even when attention was oriented toward the distinctive features of disgust (Experiment 2). Additionally, when attention was oriented toward certain zones, the eyes (which contain characteristics of anger) had the longest dwell times, followed by the nose (which contains characteristics of disgust; Experiment 2). Thus, although participants may attend to the distinguishing features of disgust and anger, these may not aid them in accurately recognising each prototype.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.822
Threshold uncertainty score0.157

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.205
GPT teacher head0.330
Teacher spread0.125 · 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

Citations8
Published2023
Admission routes2
Has abstractyes

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