Fairness, prosociality, hypocrisy, and happiness: Children's and adolescents’ motives for showing unselfish behaviour and positive emotions
Bibliographic record
Abstract
The present study examined what motives account for age-related decreases in selfish behaviour and whether these motives equally predict positive emotions when making a moral decision. The study was based on a sample of 190 children and adolescents (101 females) from three different age groups (childhood, early adolescence, and middle adolescence, M = 12.9 years, SD = 2.58). A decision-making task was used where participants chose between (1) maximizing their own self-interest versus (2) being prosocial, (3) being fair, or (4) appearing fair while avoiding the costs of actually being fair. Overall, prosociality and fairness were equally important motives for unselfish behaviour. At the same time, the importance of fairness motivation increased with age. Hypocrisy motivation was less frequent than expected by chance. Prosociality was most strongly and positively associated with self-rated happiness about the decision, whereas the opposite was found for individuals who were motivated by fairness. Overall, the study indicates that children's or adolescents' unselfish behaviour in decision-making tasks are driven by a variety of motives with diverse emotional implications. The relative importance of these motives changes over the course of development. STATEMENT OF CONTRIBUTION: What is already known on this subject? Older children behave less selfishly in resource allocation tasks. Prosocial behaviour is associated with positive emotions. What the present study adds? Unselfish behaviour is equally motivated by fairness and prosociality. Fairness motivation increases from childhood throughout adolescence. Decisions motivated by prosociality are experienced as more positive than decisions motivated by fairness.
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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 | 0.003 |
| 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 | 0.000 |
| 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".