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Record W4402904669 · doi:10.1167/jov.24.10.720

Identifying Other-Race Faces: It’s Less in the Eyes.

2024· article· en· W4402904669 on OpenAlexaff
Anthony Proulx, Isabelle Charbonneau, Justin Duncan, Vicki Ledrou-Paquet, Caroline Blais, Daniel Fiset

Bibliographic record

VenueJournal of Vision · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsUniversité du Québec en Outaouais
Fundersnot available
KeywordsRace (biology)OptometryGeologyMedicinePaleontology

Abstract

fetched live from OpenAlex

Most studies in face recognition have focused on how individuals identify faces within their own ethnic group, highlighting the crucial role of the eye region in face identification (Vinette et al., 2004; Butler et al., 2010; Royer et al., 2018). Nevertheless, the general population encounters difficulties in identifying individuals from a different ethnicity, a phenomenon known as the “other-race effect” (ORE; Meissner & Brigham, 2001). Despite decades of investigation, the perceptual mechanisms associated with ORE remain inadequately understood. It is plausible that different perceptual strategies are employed in identifying other-race faces. In this study, 21 White participants initially learned to identify 8 Black and 8 White faces. Subsequently, they were tasked to recognize these same faces presented through small Gaussian apertures (“Bubbles”; Gosselin & Schyns, 2002). We also measured the extent of the ORE using an old/new recognition task. Collectively, participants exhibited an ORE, evidenced by a higher d' with own-race faces (μd=0.26, σd=0.27, t(20)=4.32, p<.001, d=0.94). Regarding the Bubbles results, Pixel Tests (p<.05; Stat4Ci Toolbox; Chauvin et al., 2005) revealed a significant reliance on the eyes and the mouth for faces of both race. Crucially, comparison between classification images of own- and other-race faces unveiled significant differences: greater eye reliance (z-score difference of 12.52) for own-race faces and increased nose and mouth reliance (z-score differences of -5.58 and -5.94, respectively) for other-race faces. We hypothesize that, at least as a group effect, the ORE may arise from diminished eye reliance and an excessive dependence on facial features associated with ethnic information (Levin, 1996). Additional participants, including a more diverse sample (e.g., African participants), are currently undergoing testing to explore individual and cultural differences in perceptual strategies employed to recognize own- and other-race faces.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.003
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.089
GPT teacher head0.459
Teacher spread0.370 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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