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Preschoolers’ sensitivity to referential ambiguity: evidence for a dissociation between implicit understanding and explicit behavior

2008· article· en· W2135449073 on OpenAlexafffund
Elizabeth S. Nilsen, Susan A. Graham, Shannon J. Smith, Craig G. Chambers

Bibliographic record

VenueDevelopmental Science · 2008
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsUniversity of TorontoUniversity of Calgary
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsAmbiguityPsychologyDissociation (chemistry)Perspective (graphical)Cognitive psychologyMeaning (existential)Eye movementDevelopmental psychologySocial psychologyLinguisticsArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

Four-year-olds were asked to assess an adult listener's knowledge of the location of a hidden sticker after the listener was provided an ambiguous or unambiguous description of the sticker location. When preschoolers possessed private knowledge about the sticker location, the location they chose indicated that they judged a description to be unambiguous even when the message was ambiguous from the listener's perspective. However, measures of implicit awareness (response latencies and eye movement measures) demonstrated that even when preschoolers had private knowledge about the sticker location, ambiguous messages led to more consideration of an alternative location and longer response latencies than unambiguous messages. The findings demonstrate that children show sensitivity to linguistic ambiguity earlier than previously thought and, further, that they can detect linguistic ambiguity in language directed to others even when their own knowledge clarifies the intended meaning.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.268
GPT teacher head0.394
Teacher spread0.126 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations53
Published2008
Admission routes2
Has abstractyes

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