An age bias in other-race face perception: neural and behavioral evidence
Bibliographic record
Abstract
The other-race effect (ORE) refers to a well-documented disadvantage in recognizing faces of other races compared to one’s own. Yet, the perceptual biases underlying ORE remain less understood. This research combines behavioral similarity measures, neural decoding and style-based generative adversarial networks (StyleGAN2; Karras et al., 2020) to examine visual biases, with focus on age misrepresentation in other-race (OR) face perception. To this end, first, East Asian and White participants (n = 190) rated the pairwise visual similarity of OR and same-race (SR) synthetically generated faces. The similarity structure of behavioral data was mapped onto that of StyleGAN2 latent representations. Relying on this mapping, we employed a novel procedure for GAN-based image reconstruction to recover SR and OR face percepts across our participants. The procedure generated hyper-realistic visualizations of face percepts. More importantly, facial attribute analysis, using RetinaFace (Deng et al., 2019), revealed that OR image reconstructions appeared systematically younger than SR ones. Second, electroencephalography (EEG) data were collected from East Asian and White participants (n = 40) who viewed OR and SR faces. Neural representations of these faces, recovered through EEG-based face decoding and image reconstruction (Nemrodov et al., 2018), were subsequently rated by another group of East Asian and White validators (n = 46) for perceived age and typicality. OR face reconstructions were consistently rated as younger and more typical for their race than SR reconstructions across both validator groups. Together, these findings provide convergent evidence for age misrepresentation in OR face perception across different data types and different reconstruction methodologies. These results open new avenues for investigating representational differences in face perception and highlight new implications for cross-racial interactions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".