Accuracy and consensus in judgments of trustworthiness from faces: Behavioral and neural correlates.
Bibliographic record
Abstract
Perceivers' inferences about individuals based on their faces often show high interrater consensus and can even accurately predict behavior in some domains. Here we investigated the consensus and accuracy of judgments of trustworthiness. In Study 1, we showed that the type of photo judged makes a significant difference for whether an individual is judged as trustworthy. In Study 2, we found that inferences of trustworthiness made from the faces of corporate criminals did not differ from inferences made from the faces of noncriminal executives. In Study 3, we found that judgments of trustworthiness did not differ between the faces of military criminals and the faces of military heroes. In Study 4, we tempted undergraduates to cheat on a test. Although we found that judgments of intelligence from the students' faces were related to students' scores on the test and that judgments of students' extraversion were correlated with self-reported extraversion, there was no relationship between judgments of trustworthiness from the students' faces and students' cheating behavior. Finally, in Study 5, we examined the neural correlates of the accuracy of judgments of trustworthiness from faces. Replicating previous research, we found that perceptions of trustworthiness from the faces in Study 4 corresponded to participants' amygdala response. However, we found no relationship between the amygdala response and the targets' actual cheating behavior. These data suggest that judgments of trustworthiness may not be accurate but, rather, reflect subjective impressions for which people show high agreement.
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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 | 0.003 | 0.034 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".