Does Pretending Optimize Young Children’s Future-Oriented Decision-Making?
Bibliographic record
Abstract
Preschoolers often make more optimal future-oriented decisions for another person than for themselves (i.e., other-over-self advantage). This advantage may reflect psychological distance from the self, reducing children’s tendency to be biased by their current state. Two experiments explored whether other distancing techniques, pretense/role-playing specifically, benefit children’s performance on a Delay of Gratification (DoG) task and a Preferences task (in which children are asked to predict whether they/another child will prefer an adult- or child-preferable item when “all grown up”). In Experiment 1A, we tested whether pretending to be another child confers a similar advantage as choosing for another child for 44- to 64-month-olds (N = 98) residing in Ottawa, Canada. While children performed better than chance when choosing for a peer on the Preferences task (p < 0.001), pretending to be a peer did not boost children’s performance relative to chance. A further 27 preschoolers from Ottawa (Experiment 1B) completed the Preferences task while pretending to be an adult and asked about current preferences. Preschoolers highly favored the child items (p = 0.002), suggesting that even pretending to be an adult did not improve task performance. We discuss the implications of our findings in the contexts of psychological distancing, pretending, and future directions for improving future-oriented reasoning in early childhood.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".