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Record W2154443039 · doi:10.1177/0165025412467584

Development of face scanning for own- and other-race faces in infancy

2012· article· en· W2154443039 on OpenAlex
Wen Xiao, Naiqi G. Xiao, Paul C. Quinn, Gizelle Anzures, Kang Lee

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

VenueInternational Journal of Behavioral Development · 2012
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsUniversity of Toronto
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsRace (biology)PsychologyFixation (population genetics)Developmental psychologyEye trackingAudiologyEye movementDemographyMedicineArtificial intelligencePopulation

Abstract

fetched live from OpenAlex

The present study investigated whether infants visually scan own- and other-race faces differently as well as how these differences in face scanning develop with age. A multi-method approach was used to analyze the eye-tracking data of 6- and 9-month-old Caucasian infants scanning dynamically displayed own- and other-race faces. We found that 6-month-olds showed differential fixation, fixating significantly more on the left eye and mouth of own-race faces, but more on the nose of other-race faces. Infants at 9 months of age fixated more on the eyes of own-race faces, but more on the mouth of other-race faces. A scan path analysis revealed that infants shifted their attention between the eyes of the own-race faces significantly more frequently than for other-race faces. Overall, younger and older infants responded differentially to own- versus other-race faces not only in the absolute amount of time spent fixating specific features, but also on their fixation shifts between features.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.408
Threshold uncertainty score0.363

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.130
GPT teacher head0.392
Teacher spread0.262 · 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