[no title]
Bibliographic record
Abstract
Face processing has been considered almost a unique and universal biological perceptual skill shared by human beings. People from different cultures identify familiar faces with little effort from a virtually unlimited number of different faces. Classification image techniques (Bubbles - Gosselin & Schyns, 2001; Caldara et al., 2005) and eye movement patterns (Henderson et al., 2001) have objectively identified the critical information used by the face system to achieve this complex perceptual categorization process: the eyes. However, literature on face processing has so far largely ignored culture. Evidence supporting cultural differences in perceptual strategies for affording visual information has expanded rapidly in the past decade. Westerners tend to engage analytic perceptual mechanisms for processing the visual environment, whereas East Asians use holistic perceptual mechanisms. Whether such perceptual cultural differences would expand to the biologically relevant class of faces and the important task of face identification remain to be clarified. In the present study, we investigated human eye movements of Westerners Caucasian and East Asian participants while they encoded and identified Caucasian and Asian faces. As expected, participants showed better performance for same- than other-race face identification. Strikingly, however, Caucasian and Asian participants used different but consistent scan patterns to adapt to the task, regardless of the race of the faces. Caucasians fixated more focally on the eye region than Asians during face encoding and identification stages, whereas Asians attended more to the central region of the face. These results demonstrate that face processing can no longer be considered as arising from a unique and universal series of perceptual events. Culture tunes eye movements for achieving face identification by using different visual information. These biological perceptual skills may be shaped by visual experience during social interactions and other cultural factors, and cannot be explained completely by the differences in faces per se.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.074 | 0.034 |
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".