Young Children Have Difficulty Predicting Future Preferences in the Presence of a Conflicting Physiological State
Bibliographic record
Abstract
This study examined children's predictions about their future preferences when they were in two different physiological states (thirsty and not thirsty). Ninety 3‐ to 7‐year‐olds were asked to predict what they would prefer tomorrow: pretzels to eat or water to drink after having consumed pretzels, and again after having had the opportunity to quench their thirst with water. Results showed that although children initially preferred pretzels to water at baseline, they more often indicated that they would prefer water the next day after they had consumed pretzels. After consuming water, however, the same children indicated they would prefer pretzels the next day. Children's verbal justifications for their choices rarely made reference to their current or future states, but rather justifications were more likely to make reference to their general preferences when they were no longer thirsty compared to when they were thirsty. Results suggest that current physiological states have a powerful influence on future preferences. The findings are discussed in the context of the development of episodic foresight, the Bischof‐Kohler hypothesis, and the important and often overlooked role that children's current states play in future decision making. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".