What will you want tomorrow? Children—But not adults- mis-predict another person’s future desires
Bibliographic record
Abstract
Young children have difficulty predicting a future physiological state that conflicts with their current state. This finding is explained by the fact that children are biased by their current state (e.g., thirsty and desiring water) and thus have difficulty imagining themselves in a different state (e.g., not thirsty and desiring pretzels) "tomorrow," for example. Another potential explanation that we explore here is that young children have difficulty understanding how physiological states, like thirst, fluctuate over time. We asked 3-, 4- and 5-year-olds (Experiment 1) and adults (Experiment 2) to predict what a thirsty Experimenter-who preferred crisps to water-would want ("water" or "crisps") "right now" and "tomorrow." Only adults correctly predicted someone else's future desires when this person's future and current desires conflicted. In contrast, both adults and children in the control groups (in which the Experimenter was not thirsty) had no difficulty predicting that the Experimenter would want crisps "right now" and "tomorrow." Our findings suggest that children's difficulty predicting future desires cannot solely be attributed to their being biased by their current state since the children in our study were, themselves, not thirsty. We discuss our results in the context of children's difficulty understanding fluctuations in physiological states.
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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 | 0.010 |
| 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 | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".