Race differences in eye movements to three-quarter view faces
Bibliographic record
Abstract
A number of studies have shown a difference between Asian and Caucasian participants in fixation patterns to faces. Caucasian participants’ fixations are concentrated on the eyes, whereas Asian participants fixate more centrally on the nose region. All previous studies exploring race differences have used front view faces. The present study investigated whether this preference for fixating the nose in Asian participants extends to three-quarter (mid-profile) view faces. Eye movements were monitored during the learning and recognition phases of a memory task for three-quarter view Asian and Caucasian faces. We found Hong Kong Chinese participants predominantly fixated both the nose and central-eye (eye closest to the observer) regions of the three-quarter view faces at both study and test. There was no difference in the proportion of fixations to the nose and the central eye, and we found that Asian and Caucasian faces elicited similar fixation patterns. These results differ from previous findings for front view faces where Asian participants showed a significantly greater proportion of fixations towards the nose than the eyes. The results also differ from recent findings for three-quarter view faces in Caucasian participants where the central eye received a far higher proportion of fixations than the nose. These results suggest that race influences fixation patterns to non-frontal views of faces. 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 | 0.000 | 0.002 |
| 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) | 0.004 | 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".