Facial impressions of niceness influence children’s interpretations of peers’ ambiguous behavior
Bibliographic record
Abstract
Children infer personality traits from faces when they are asked explicitly which face appears nice or mean. Less is known about how children use face-trait information implicitly to make behavioral evaluations. We used the Ambiguous Situations Protocol to explore how children use face-trait information to form interpretations of ambiguous situations when the behavior or intention of the target child was unclear. On each trial, children (N = 144, age range = 4-11.95 years; 74 girls, 67 boys, 3 gender not specified; 70% White, 10% other or mixed race, 5% Asian, 4% Black, 1% Indigenous, 9% not specified) viewed a child's face (previously rated high or low in niceness) before seeing the child's face embedded within an ambiguous scene (Scene Task) or hearing a vignette about a misbehavior done by that child (Misbehavior Task). Children described what was happening in each scene and indicated whether each misbehavior was done on purpose or by accident. Children also rated the behavior of each child and indicated whether the child would be a good friend. Facial niceness influenced children's interpretations of ambiguous behavior (Scene Task) by 4 years of age, and ambiguous intentions (Misbehavior Task) by 6 years. Our results suggest that the use of face-trait cues to form interpretations of ambiguous behavior emerges early in childhood, a bias that may lead to differential treatment for peers perceived with a high-nice face versus a low-nice face.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".