Is the face a window to the soul? Investigation of the accuracy of intuitive judgments of the trustworthiness of human faces.
Bibliographic record
Abstract
Although trustworthiness judgments based on a stranger's face occur rapidly (Willis & Todorov, 2006), their accuracy is unknown. We examined the accuracy of trustworthiness judgments of the faces of 2 groups differing in trustworthiness (Nobel Peace Prize recipients/humanitarians vs. America's Most Wanted criminals). Participants viewed 34 faces each for 100 ms or 30 s and rated their trustworthiness. Subsequently, participants were informed about the nature of the 2 groups and estimated group membership for each face. Judgments formed with extremely brief exposure were similar in accuracy and confidence to those formed after a long exposure. However, initial judgments of untrustworthy (criminals') faces were less accurate (M = 48.8%) than were those of trustworthy faces (M = 62.7%). Judgment accuracy was above chance for trustworthy targets only at Time 1 and slightly above chance for both target types at Time 2. Participants relied on perceived kindness and aggressiveness to inform their rapidly formed intuitive decisions. Thus, intuition plays a minor facilitative role in reading 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 | 0.002 | 0.024 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".