Situational affordances constrain first impressions from faces
Bibliographic record
Abstract
Humans spontaneously attribute a rich variety of traits (e.g., trustworthy, competent) to strangers based on facial appearance. Despite decades of research on these facial first impressions, few studies have investigated how situational affordances relevant to human perceivers impact impression formation. Nearly all existing research comes from participants forming impressions of targets who bear no relevance (real or manipulated) to the participant. Here, we tested whether situational affordances (i.e., opportunities or obstacles to fulfilling one’s goals) related to three fundamental social motives—mate-seeking, self-protection, and disease avoidance—constrain the way that perceivers form impressions from faces. Across 167,951 ratings from 400 Canadian undergraduates, situational affordances caused the structure of facial impressions to change, generally becoming more constrained when targets were rated in goal-relevant contexts versus a goal-neutral context absent any affordances. These changes may arise from participants forming impressions on one central, goal-relevant trait, which influences ratings on other less-relevant traits.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".