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Record W2506567064 · doi:10.1177/0301006616661913

The Influence of Averageness on Adults’ Perceptions of Attractiveness: The Effect of Early Visual Deprivation

2016· article· en· W2506567064 on OpenAlexaff
Larissa Vingilis‐Jaremko, Daphne Maurer, Gillian Rhodes, Linda Jeffery

Bibliographic record

VenuePerception · 2016
Typearticle
Languageen
FieldPsychology
TopicEvolutionary Psychology and Human Behavior
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAttractivenessPerceptionPsychologyVisual attentionCognitive psychologyVisual perceptionDevelopmental psychologyNeuroscience

Abstract

fetched live from OpenAlex

Adults who missed early visual input because of congenital cataracts later have deficits in many aspects of face processing. Here we investigated whether they make normal judgments of facial attractiveness. In particular, we studied whether their perceptions are affected normally by a face's proximity to the population mean, as is true of typically developing adults, who find average faces to be more attractive than most other faces. We compared the judgments of facial attractiveness of 12 cataract-reversal patients to norms established from 36 adults with normal vision. Participants viewed pairs of adult male and adult female faces that had been transformed 50% toward and 50% away from their respective group averages, and selected which face was more attractive. Averageness influenced patients' judgments of attractiveness, but to a lesser extent than controls. The results suggest that cataract-reversal patients are able to develop a system for representing faces with a privileged position for an average face, consistent with evidence from identity aftereffects. However, early visual experience is necessary to set up the neural architecture necessary for averageness to influence perceptions of attractiveness with its normal potency.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.268
Threshold uncertainty score0.796

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.015
GPT teacher head0.340
Teacher spread0.325 · 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 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

Citations1
Published2016
Admission routes1
Has abstractyes

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