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Record W2232940917 · doi:10.1111/bjdp.12130

Older (but not younger) preschoolers understand that knowledge differs between people and across time

2016· article· en· W2232940917 on OpenAlexaff
Julian S. Caza, Cristina M. Atance, Daniel M. Bernstein

Bibliographic record

VenueBritish Journal of Developmental Psychology · 2016
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsKwantlen Polytechnic UniversityUniversity of Ottawa
Fundersnot available
KeywordsPsychologyGeneral knowledgeDevelopmental psychologyCognitionCognitive developmentSelf-knowledgeKnowledge levelChild developmentSocial psychology

Abstract

fetched live from OpenAlex

We examined 3- to 5-year-olds' understanding of general knowledge (e.g., knowing that clocks tell time) by investigating whether (1) they recognize that their own general knowledge has changed over time (i.e., they knew less as babies than they know now), and (2) such intraindividual knowledge differences are easier/harder to understand than interindividual differences (i.e., Do preschoolers understand that a baby knows less than they do?). Forty-eight 3- to 5-year-olds answered questions about their current general knowledge ('self-now'), the general knowledge of a 6-month-old ('baby-now'), and their own general knowledge at 6 months ('self-past'). All age groups were significantly above chance on the self-now questions, but only 5-year-olds were significantly above chance on the self-past and baby-now questions. Moreover, children's performance on the baby-now and self-past questions did not differ. Our findings suggest that younger preschoolers do not fully appreciate that their past knowledge differs from their current knowledge, and that others may have less knowledge than they do. We situate these findings within the research on knowledge understanding, more specifically, and cognitive development, more broadly.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.459
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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.

Opus teacher head0.031
GPT teacher head0.313
Teacher spread0.282 · 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; a candidate call from one teacher head, not a consensus.

Study designObservational
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

Citations7
Published2016
Admission routes1
Has abstractyes

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