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Record W6920742416 · doi:10.6084/m9.figshare.23442731

Children’s Pursuit of Counterintuitive Information in Books

2023· article· en· W6920742416 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2023
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsnot available
Fundersnot available
KeywordsCounterintuitivePreferenceAsideCognitionFunction (biology)Philosophy of science

Abstract

fetched live from OpenAlex

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 (<i>N</i> = 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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.870
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0870.012

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.

Opus teacher head0.025
GPT teacher head0.284
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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