Evaluating Trustworthiness: Differences in Visual Representations as a Function of Face Ethnicity
Bibliographic record
Abstract
Trustworthiness is rapidly and automatically assessed based on facial appearance, and it is one of the main dimensions of face evaluation (Oosterhof & Todorov, 2008). Few studies have investigated how we evaluate trustworthiness in faces of other ethnicities. The present study aimed at comparing how individuals imagine a trustworthy White or Black face. More specifically, the mental representations of a trustworthy White and Black face were revealed in 30 participants using Reverse Correlation (Mangini & Biederman, 2004). On each trial (500 per participant), two stimuli, created by adding sinusoidal white noise to an identical base face (White or Black, depending on the experimental condition), were presented side-by-side. The participant’s task was to decide which of the two looked most trustworthy. The noise patches corresponding to the chosen stimuli were summed to produce a classification image, representing the luminance variations associated with a percept of trustworthiness. A statistical threshold was found using the Stat4CI’s cluster test (Chauvin et al., 2005), a method that corrects for the multiple comparisons across all pixels while taking into account the spatial dependence inherent to coherent images (tcrit=3.0, k=246, p< 0.025). Results show that for a White face, perception of trustworthiness is associated with a lighter eye region; for a Black face, perception of trustworthiness is associated with a darker right eye and a lighter mouth. Statistically comparing both classification images (tcrit=3.0, k=246, p< 0.025) revealed that the eye region was more important in judging trustworthiness of White faces, while the mouth region was more important for Black faces. The present study shows that facial traits used to form the mental representation of trustworthiness differ with face ethnicity. More research will be needed to verify if this finding generalizes across populations of different ethnicities.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".