Potent features for the categorization of Caucasian, African American, and Asian faces in Caucasian observers
Bibliographic record
Abstract
What is the information mediating race categorization? Here, we applied the Bubbles technique (Gosselin & Schyns, 2001) to reveal which areas of faces at five different spatial scales are efficient for race categorization in Caucasian participants. We asked 30 participants to categorize 700 “bubblized” faces selected randomly from sets of 100 male Caucasian faces, 100 male African American faces, and 100 male Asian faces. All face photos were normalized using SHINE, a new Matlab algorithm for luminance and power spectrum equalization (Willenbockel et al., in preparation). Separate multiple linear regressions between information samples and accuracy were performed for each race. The resulting classification images reveal the potent features for the categorization of Caucasian, African American, and Asian faces in Caucasian observers. For African American faces, the participants used mainly the nose and the mouth in the spatial frequency (SF) bands ranging from 10 to 42 cycles per face width. For Asian faces, they used the eyes in the SF bands ranging from 10 to 84 cycles per face width and the mouth in the SF band ranging from 5 to 10 cycles per face width. For Caucasian faces, they efficiently employed the eyes in the SF bands ranging from 5 to 21 cycles per face width as well as the mouth and the region between the eyes in the second highest SF band ranging from 21 to 42 cycles per face width. Interestingly, and congruently with the results of Smith et al. (2005), we observed almost no overlap between the information used for each stimulus category.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".