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Record W1974756624 · doi:10.1080/02643290442000383

Can perceptual expertise accountfor the own-race bias in face recognition? A split-brain study

2005· article· en· W1974756624 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

VenueCognitive Neuropsychology · 2005
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPsychologyRace (biology)Cognitive psychologyPerceptionFace perceptionFacial recognition systemFace (sociological concept)AudiologyDevelopmental psychologyNeuroscienceLinguisticsPattern recognition (psychology)Medicine

Abstract

fetched live from OpenAlex

The own-race bias (ORB) in facial recognition is characterised by increased accuracy in recognition of individuals from one's own racial group, relative to individuals from other racial groups. Here we report data from a split-brain patient indicating that the ORB may be tied to functions lateralised in the right cerebral hemisphere. Patient JW (a Caucasian) performed a delayed match-to-sample task for faces that varied both the race of the facial memoranda-Caucasian or Japanese-and the cerebral hemisphere performing the task. While JW's left hemisphere showed no effect of race on facial recognition, his right hemisphere demonstrated a significant performance advantage for Caucasian faces. These findings are discussed in relation to stimulus familiarity and the development of perceptual expertise.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.893
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.004

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 head0.173
GPT teacher head0.378
Teacher spread0.206 · 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