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Record W2578635980 · doi:10.1080/15248372.2017.1281279

Enhancing Young Infants’ Representations of Physical Events Through Improved Retrieval (Not Encoding) of Information

2017· article· en· W2578635980 on OpenAlexafffund
Su‐hua Wang, Kristine H. Onishi

Bibliographic record

VenueJournal of Cognition and Development · 2017
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsMcGill University
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsPsychologyEncoding (memory)Cognitive psychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

Infants’ representations of physical events are surprisingly flexible. Brief exposure to one event can immediately enhance infants’ representations of another event. The present experiments tested two potential mechanisms underlying this priming: enhanced encoding or improved retrieval. Five-month-olds saw a target block become hidden inside a container, followed by priming events that involved a second block. The target was subsequently withdrawn from the container. Infants noticed a change to the target’s height after seeing priming events involving occlusion, but they failed to so do if priming events involved no occlusion (Experiment 1). Infants noticed the change even when the priming and target blocks were not identical (Experiment 2). Because the target became fully hidden before the priming events started, priming must have arisen from improved retrieval, not enhanced encoding, of information about the target. The results add to our understanding of how brief observation of one event can affect infants’ processing of subsequent events, thereby elucidating fine-grained aspects of the representational process.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.027
GPT teacher head0.332
Teacher spread0.305 · 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

Citations9
Published2017
Admission routes2
Has abstractyes

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