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Record W4376121489 · doi:10.1111/cdev.13940

Memories of structured input become increasingly distorted across development

2023· article· en· W4376121489 on OpenAlexafffundabout
Tess Allegra Forest, Zahra Abolghasem, Amy S. Finn, Margaret L. Schlichting

Bibliographic record

VenueChild Development · 2023
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of CanadaUniversity of TorontoCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsPsychologyDevelopmental psychologyMemory developmentCognitionCognitive developmentMemory testTest (biology)Childhood memoryYoung adultAge groupsEpisodic memoryCognitive psychologyDemography

Abstract

fetched live from OpenAlex

Abstract Trajectories of cognitive and neural development suggest that, despite early emergence, the ability to extract environmental patterns changes across childhood. Here, 5- to 9-year-olds and adults (N = 211, 110 females, in a large Canadian city) completed a memory test assessing what they remembered after watching a stream of shape triplets: the particular sequence in which the shapes occurred and/or their group-level structure. After accounting for developmental improvements in overall memory, all ages remembered specific transitions, while memory for group membership was only observed in older children and adults (age by test-type interaction η2 = .05). Thus, while young children form memories for specifics of structured experience, memory for derived associations is refined later—underscoring that adults and young children form different memories despite identical experience.

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)0.002
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 score0.043
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.021
GPT teacher head0.299
Teacher spread0.278 · 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

Citations10
Published2023
Admission routes3
Has abstractyes

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