‘Things aren't so bad!’: Preschoolers overpredict the emotional intensity of negative outcomes
Bibliographic record
Abstract
Adults often overpredict the emotional intensity of future events, but little is known about whether this 'intensity bias' is present in early childhood. We asked 48 3- to 5-year-olds to (1) predict and (2) report their emotions concerning two desirable (receiving four stickers, scoring up to two points in a ball toss) and two undesirable (receiving one sticker, scoring no points) outcomes. Children showed the intensity bias by overpredicting how negatively they would feel if they received one sticker, but not for scoring no points. We discuss how task factors (e.g., personal volition) and cognitive mechanisms (e.g., immune neglect) may influence children's tendency to show the intensity bias. Statement of contribution What is already known on this subject? Adults tend to overpredict the intensity of their emotional reactions to future events. Whether similar 'affective forecasting' errors characterize preschoolers' predictions is not known. What does this study add? We created two forecasting tasks ('sticker' and 'ball') with both desirable and undesirable outcomes. We obtained evidence for a 'negativity' but not a 'positivity' bias in children's predictions. On the sticker task, children overpredicted how badly they would feel after receiving one, versus, four stickers.
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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 | 0.000 | 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".