The facial information underlying economic decision-making
Bibliographic record
Abstract
Faces are rapidly and automatically assessed on multiple social dimensions, including trustworthiness. The high inter-rater agreement on this social judgment suggests a systematic association between facial appearance and perceived trustworthiness. The facial information used by observers during explicit trustworthiness judgments has been studied before. However, it remains unknown whether the same perceptual strategies are used during decisions that involve trusting another individual, without necessitating an explicit trustworthiness judgment. To explore this, 53 participants completed the Trust Game, an economic decision task, while facial information was randomly sampled using the Bubbles method. Our results show that economic decisions based on facial cues rely on similar visual information as that used during explicit trustworthiness judgments. We then manipulated facial features identified as diagnostic for trust to test their influence on perceived trustworthiness (Experiment 2) and on trust-related behaviors (Experiment 3). Across all experiments, subtle, targeted changes to facial features systematically shifted both impressions and monetary trust decisions. These findings demonstrate that the same perceptual strategies underlie explicit judgments and trust behaviors, highlighting the applied relevance of even minimal alterations in facial appearance. These findings should be replicated with real faces from diverse demographic backgrounds to confirm their generalizability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 | 0.004 |
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; both teacher heads agree on what is shown here.
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".