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Becoming a Face Expert

2006· article· en· W2020945199 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePsychological Science · 2006
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsMcMaster UniversityBrock University
Fundersnot available
KeywordsPsychologyPerceptionFace perceptionCognitive psychologyFace (sociological concept)Facial recognition systemVisual perceptionDevelopmental psychologyPattern recognition (psychology)Neuroscience

Abstract

fetched live from OpenAlex

Expertise in recognizing facial identity, and, in particular, sensitivity to subtle differences in the spacing among facial features, improves into adolescence. To assess the influence of experience, we tested adults and 8-year-olds with faces differing only in the spacing of facial features. Stimuli were human adult, human 8-year-old, and monkey faces. We show that adults' expertise is shaped by experience: They were 9% more accurate in seeing differences in the spacing of features in upright human faces than in upright monkey faces. Eight-year-olds were 14% less accurate than adults for both human and monkey faces (Experiment 1), and their accuracy for human faces was not higher for children's faces than for adults' faces (Experiment 2). The results indicate that improvements in face recognition after age 8 are not related to experience with human faces and may be related to general improvements in memory or in perception (e.g., hyperacuity and spatial integration).

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.340
Threshold uncertainty score1.000

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.149
GPT teacher head0.414
Teacher spread0.265 · 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