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Record W2968777038 · doi:10.3389/fpsyg.2019.02006

Individual Differences in Children’s Preference to Learn From a Confident Informant

2019· article· en· W2968777038 on OpenAlexafffund
Aimie-Lee Juteau, Isabelle Cossette, Marie-Pier Millette, Patricia É. Brosseau-Liard

Bibliographic record

VenueFrontiers in Psychology · 2019
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Ottawa
KeywordsPsychologyPreferenceSituational ethicsTest (biology)Session (web analytics)Developmental psychologyStability (learning theory)Social psychologyStatistics

Abstract

fetched live from OpenAlex

Past research has demonstrated that children can use an informant’s confidence level to selectively choose from whom to learn. Yet, in any given study, not all children show a preference to learn from the most confident informant. Are individual differences in this preference stable over time and across learning situations? In two studies, we evaluated the stability of preschoolers’ performance on selective learning tasks using confidence as a cue. The first study (N=48) presented children with the same two informants, one confident and one hesitant, and the same four test trials twice with a one-week delay between administrations. The second study (N=50) presented two parallel tasks with different pairs of informants and test trials one after the other in the same testing session. Correlations between administrations were moderate in the first study and small in the second study, suggesting that children show some stability in their preference to learn from a confident individual but that their performance is also influenced by important situational factors, measurement error or both. Implications for the study of individual differences in selective social learning are discussed.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.023
GPT teacher head0.280
Teacher spread0.257 · 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 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

Citations10
Published2019
Admission routes2
Has abstractyes

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