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Record W4234277757 · doi:10.32920/ryerson.14649888.v1

The Effects of Facial Attractiveness on Spontaneous Facial Mimicry

2021· preprint· en· W4234277757 on OpenAlexaff
Katlyn Peck

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicEvolutionary Psychology and Human Behavior
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMimicryAttractivenessPsychologyFacial electromyographyFacial musclesFacial expressionFacial attractivenessPerceptionCognitive psychologyCommunicationNeuroscienceBiologyZoology

Abstract

fetched live from OpenAlex

When individuals are presented with emotional facial expressions they spontaneously react with brief, distinct facial movements that ‘mimic’ the presented faces. While the effects of facial mimicry on emotional perception and social bonding have been well documented, the role of facial attractiveness on the elicitation of facial mimicry is unknown. We hypothesized that facial mimicry would increase with more attractive faces. Facial movements were recorded with electromyography upon presentation of averaged and original stimuli while ratings of attractiveness and intensity were obtained. In line with existing findings, emotionally congruent responses were observed in relevant facial muscle regions. Unexpectedly, the strength of observers’ facial mimicry responses decreased with more averaged faces, despite being rated perceptually as more attractive. These findings suggest that facial attractiveness moderates the degree of facial mimicry muscle movements elicited in observers. The relationship between averageness, attractiveness and mimicry is discussed in light of this counterintuitive finding.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
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)0.0020.000

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 head0.028
GPT teacher head0.337
Teacher spread0.309 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2021
Admission routes1
Has abstractyes

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