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Record W2099763206 · doi:10.1068/p7212

The Composite-Face Effect Survives Asymmetric Face Distortions

2012· article· en· W2099763206 on OpenAlexaff
Adélaïde de Heering, Jessica Wallis, Daphne Maurer

Bibliographic record

VenuePerception · 2012
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsMcMaster University
Fundersnot available
KeywordsFace (sociological concept)Distortion (music)PsychologySimilarity (geometry)Composite numberFace perceptionCognitive psychologyComputer scienceArtificial intelligencePerceptionImage (mathematics)NeurosciencePhilosophyAlgorithmLinguistics

Abstract

fetched live from OpenAlex

In two experiments, we investigated whether adults use holistic processing even for faces that are grossly distorted because their eyes have been moved asymmetrically to violate the common layout of a face (distorting its first-order relations). To this end we used a compelling demonstration that faces are processed as wholes, the composite-face effect. Specifically, adults judged the similarity of sequentially presented top halves of normal (original condition) and distorted faces with one eye (one-eye condition) or two eyes (two-eyes condition) shifted up by an abnormal amount. Trials were either blocked by type of distortion (experiment 1) or intermixed within the experiment (experiment 2). In both experiments, participants demonstrated a composite-face effect of the same magnitude in the three conditions, a pattern suggesting that they processed holistically even faces whose first-order relations were violated.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.039
GPT teacher head0.306
Teacher spread0.267 · 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 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

Citations13
Published2012
Admission routes1
Has abstractyes

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