Categorizing racially ambiguous faces as own- versus other-race influences how those faces are scanned
Bibliographic record
Abstract
Recent eye-tracking studies have revealed that own- and other-race faces are scanned differently, and this differential scanning is affected by observer ethnicity. Westerners scan the eyes of own- and other-race faces more than other face parts (Blais et al., 2008). In contrast, Chinese observers scan more the central (nasal) region of Chinese faces, whereas they scan more the eyes of Caucasian faces (Fu et al., 2012). To better understand the relation between categorization of a face as own- versus other-race and face scanning, we conducted two experiments with Chinese participants who had no direct interaction with other-race individuals. In Experiment 1, we morphed Chinese and Caucasian faces to produce 50%-50% racially ambiguous hybrid faces. Chinese participants first sorted the hybrid faces into Chinese or Caucasian, and were then asked to remember and recognize the faces. In addition, participants were also asked to remember and recognize 100% Chinese and 100% Caucasian faces. The results with the 100% faces replicated Fu et al. (2012): Chinese observers fixated more on the nasal region of the 100% Chinese faces and the eye regions of the 100% Caucasian faces. More importantly, when the hybrid faces were classified as Chinese, participants scanned more on the nasal region, whereas when the faces were categorized as Caucasian, participants scanned more on the eyes. In Experiment 2, participants first performed a categorization task of the hybrid faces. They were then given a surprise memory test. Again, when the hybrid faces were classified as Chinese, participants scanned more on the nasal region, whereas when the faces were categorized as Caucasian, participants scanned more on the eyes. The findings suggest that although physiognomic differences between Chinese and other-race faces engender differential visual scanning, participants’ subjective categorization of face race plays an important role in driving nose-centric versus eye-centric patterns of scanning. Meeting abstract presented at VSS 2013
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".