The effect of psychological distance on young children's future predictions
Bibliographic record
Abstract
Abstract The current study examined the impact of psychological distance on children's performance on the pretzel task. In this task, children eat pretzels (inducing thirst) and then are asked to reason about future preferences (pretzels or water). Children typically perform poorly on this task, indicating a future preference for water over pretzels, potentially due to conflicting current and future states. Given past work showing that children's future reasoning is more accurate for another person, we asked 90 thirsty 3‐ to 7‐year‐olds to reason about their own and an experimenter's future preference. Results showed that thirsty children had more difficulty predicting their own future preference compared with the experimenter's. Thirstier children were more likely to predict a future preference for water. Thirst interacted with age when making a future choice for the experimenter. How psychological distance might boost episodic foresight and possible reasons for children's poor pretzel task performance are discussed. Highlights Does psychological distancing improve children's ability to make accurate future predictions when current and future states conflict? Using the Pretzel task, thirsty children were less accurate when predicting their own future preferences compared with the future preferences of another person. Psychological distancing may help children overcome their current state to reason more accurately about the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".