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Record W4402554699 · doi:10.1177/01461672241273253

Prototype Facial Response to Cute Stimuli: Expression and Recognition

2024· article· en· W4402554699 on OpenAlexaff
Makenzie J. O’Neil, Alexander Danvers, Jing Iris Hu, Michelle N. Shiota

Bibliographic record

VenuePersonality and Social Psychology Bulletin · 2024
Typearticle
Languageen
FieldPsychology
TopicEvolutionary Psychology and Human Behavior
Canadian institutionsConcordia University
Fundersnot available
KeywordsFacial expressionPsychologyFacial Action Coding SystemExpression (computer science)Emotional expressionPerceptionDevelopmental psychologyCognitive psychologySocial psychologyCommunicationNeuroscienceComputer science

Abstract

fetched live from OpenAlex

Cute, kindchenschema stimuli can evoke a suite of cognitive, physiological, and behavioral tendencies thought to promote caregiving. This research investigated facial expression elements associated with this response to cuteness and assessed the recognizability of an expression combining these elements. In Studies 1 and 2, participants at a community outreach event (Study 1, n = 19) and undergraduate students (Study 2, n = 103) showed spontaneous facial displays while watching videos/photos of baby humans and animals. These were Facial Action Coding System (FACS)-coded, revealing characteristic and statistically distinctive action unit elements of facial expression responses to cuteness. In six follow-up online studies (combined N = 962), including replications with Syrian refugees ( n = 103) and Chinese samples ( n = 222), a “cuteness prototype” expression combining all elements identified across Studies 1 and 2 (i.e., oblique brows, chin raise, lip tightening, and Duchenne smile) was commonly interpreted as a response to cuteness. These findings add to a growing literature about caregiving-focused motivational states and associated emotion/affect.

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)0.000
metaresearch head score (Gemma)0.003
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
Metaresearch0.0000.003
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 head0.086
GPT teacher head0.401
Teacher spread0.315 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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Same venuePersonality and Social Psychology BulletinSame topicEvolutionary Psychology and Human BehaviorFrench-language works237,207