Does Observers’ Ethnicity Influence Visual Strategies for Gender and Expressiveness Judgments ?
Bibliographic record
Abstract
Recent advances in cross-cultural studies emphasized the importance of including diversified groups of participants to better understand mechanisms underlying various face processing abilities, whether they reveal a difference or not (e.g. Blais et al., 2021). In visual psychophysics, little is known about visual strategies underlying face perception among Black observers. Therefore, we investigated visual strategies in Black and White participants in a Gender and Expressiveness (ExNex) tasks, using a newly validated platform, Pack & Go by VPIXX, which allows high quality psychophysic testing online. Sixty participants (15 Blacks and 45 Whites) completed both experiments (4000 trials per participant) conducted using the Bubble’s technique (Gosselin & Schyns, 2001) which samples visual information on a trial-by-trial basis using small gaussian windows in order to reveal the most useful information in any visual task. Accuracy was maintained at 75% by adjusting online the number of bubbles using QUEST (Watson & Pelli, 1983). Group performance levels were controlled by matching individual Black participants with White participants according to their final average number of bubbles in both tasks. Classification images were produced by calculating a weighted sum of the bubbles mask, using the trial-by-trial accuracy transformed into z-scores as weights. Pixel tests from the Stat4CI (Chauvin et al., 2005) toolbox revealed significant pixels associated with performance (p< .05; Zcrit = 4.05). Mainly, both groups made use of the same visual information for both race stimuli, that is, reliance on the eye in the Gender task and on the mouth in the ExNex task. Interestingly, Black participants also relied significantly more on the left eye for ExNex judgements, but only with Black stimuli. These differences and similarities in visual strategies for Black and White observers, will be discussed regarding cross-cultural differences in face perception in general.
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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 | 0.011 |
| 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 | 0.001 |
| 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 | 0.001 |
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".