When Success Is Surprising: Children’s Ability to Use Surprise to Infer Competence
Bibliographic record
Abstract
Abstract How do we learn who is good at what? Building on the idea that humans draw rich inferences from others’ emotional expressions, here we ask whether others’ surprised reactions to performance outcomes can elicit inferences about competence. Across three experiments, participants were asked to choose “who is better” in scenarios where two students performed identically on the same task but their teacher expressed surprise to only one of them. In Experiment 1 (n = 60, adults) and Experiment 2 (n = 90, 6- to 8-year-old children), participants’ responses were modulated by not only the students’ performance outcomes (success or failure) but also the teacher’s response to the outcomes (surprise or no surprise). Specifically, participants preferentially chose the student who did not elicit the teacher’s surprise as more competent when both students succeeded, but chose the student who elicited surprise when both failed. Experiment 3a (n = 150, 4- to 8-year-olds) replicated this pattern in 6- to 8-year-olds as a group—but not in 4- to 5-year-olds—with increasing robustness with age. Finally, this pattern was significantly reduced in Experiment 3b where the teacher’s surprise was directed at an irrelevant event rather than the student’s performance (n = 90, 6- to 8-year-olds). Taken together, these results suggest that even non-valenced emotional reactions to performance outcomes—being surprised at someone’s success or failure—can inform inferences about valenced qualities such as competence. More broadly, the current findings demonstrate that emotional expressions we observe in our daily lives can lead to nuanced yet consequential social judgments.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".