Descriptive Norms Influence Children’s Injunctive and Moral Norm Beliefs
Bibliographic record
Abstract
How do children integrate descriptive norms about how others commonly behave with their injunctive norm beliefs about how they should behave? Does this relationship vary depending on the type of normative behavior? We investigated these questions in 6-9-year-olds (N = 234) from the US in two preregistered studies. In Study 1, we examined whether children’s injunctive beliefs, moral evaluations, behavioral intentions, and punishment ratings were influenced by descriptive norm information that a behavior is relatively common or uncommon. In Study 2, we tested whether children updated their own beliefs in response to descriptive norm information using a within-subject, pre-post design. We also explored whether the influence of descriptive norm information varies by the type of normative behavior (Studies 1 & 2: negatively valenced conventional, positively valenced conventional, personal preferences; Study 1 only: COVID19 health behaviors). Across both studies, we found that by 6 years of age, children integrated descriptive norm information such that their average belief ratings were influenced more by common than uncommon descriptive norms for all behaviors except personal preferences. In contrast to our predictions, children did not consistently update their prior beliefs in response to novel descriptive norm information. However, when they did update, they did so to different extents depending on the normative belief measure, type of behavior, and its frequency. Our findings suggest that, by middle-childhood, children’s injunctive norm and moral beliefs are influenced by the frequency of descriptive normative information and, more broadly, point to the bottom-up influences of children’s normative beliefs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".