Caucasian and Asian observers used the same visual features for race categorisation.
Bibliographic record
Abstract
Using the Bubbles method (Gosselin & Schyns, 2001), we recently explored the visual information mediating race categorisation in Caucasian observers (Fiset et al., VSS 2008). Unsurprisingly, the results show that different visual features are essential to identifying the different races. More specifically, for African American faces, Caucasian 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 used 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. Here, we verify if the visual information subtending race categorisation differs for Asian participants. In order to do this, we asked 38 Asian participants from Southwest University in Chongqing (China) to categorise 700 "bubblized" faces randomly selected from sets of 100 male Caucasian faces, 100 male African American faces, and 100 male Asian faces. Separate multiple linear regressions between information samples and accuracy were performed for each race. The resulting classification images reveal the most important features for the categorisation of Caucasian, African American, and Asian faces by Asian observers. Comparison between observers of both races reveals nearly identical visual extraction strategies for race categorisation. These results will be discussed with respect to the literature showing differences in visual strategies employed by Asian and Caucasian observers (e.g. Blais et al., 2008). Meeting abstract presented at VSS 2012
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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".