Differential emotion attribution to neutral faces of own and other races
Bibliographic record
Abstract
Past research has demonstrated differential recognition of emotion on faces of different races. This paper reports the first study to explore differential emotion attribution to neutral faces of different races. Chinese and Caucasian adults viewed a series of Chinese and Caucasian neutral faces and judged their outward facial expression: neutral, positive, or negative. The results showed that both Chinese and Caucasian viewers perceived more Chinese faces than Caucasian faces as neutral. Nevertheless, Chinese viewers attributed positive emotion to Caucasian faces more than to Chinese faces, whereas Caucasian viewers attributed negative emotion to Caucasian faces more than to Chinese faces. Moreover, Chinese viewers attributed negative and neutral emotion to the faces of both races without significant difference in frequency, whereas Caucasian viewers mostly attributed neutral emotion to the faces. These differences between Chinese and Caucasian viewers may be due to differential visual experience, culture, racial stereotype, or expectation of the experiment. We also used eye tracking among the Chinese participants to explore the relationship between face-processing strategy and emotion attribution to neutral faces. The results showed that the interaction between emotion attribution and face race was significant on face-processing strategy, such as fixation proportion on eyes and saccade amplitude. Additionally, pupil size during processing Caucasian faces was larger than during processing Chinese faces.
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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 | 0.003 |
| 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) | 0.002 | 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".