Children’s Pursuit of Counterintuitive Information in Books
Bibliographic record
Abstract
For decades, developmental psychologists and educators have emphasized that learning about counterintuitive phenomena may be a critical driving force for cognitive development. Thus far, little is known about the specific content that children seek to enrich their knowledge. Using a novel book-choice paradigm, we directly examine children’s preference to engage with media that contains more mundane vs. more counterintuitive content. Children ranging from 3- to 8-years (N = 174), from the U.S. and Canada, were presented with pairs of books about animals. The two books in each pair were visually identical aside from their printed title. One book in each pair was described as presenting a fact that (according to validation data on children’s and adults’ beliefs in these facts) was relatively intuitive, and the other book was described as presenting a fact that was relatively counterintuitive. The youngest participants (3–4 years) demonstrated no preference in selecting books with intuitive vs. counterintuitive facts about animals, whereas older children (5-years onward) demonstrated an increasing preference for counterintuitive content. Combined with validation data on children’s and adults’ intuitions about the focal facts, these data suggest that children’s preference to seek information that adults deem counterintuitive (at least in the domain of biology) increases with age as a function of changes in the strength of children’s intuitions about what is possible.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".