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Constructive episodic simulation, self-projection, and scene construction: Investigating the mechanisms of children's episodic thinking

2025· article· en· W4417524307 on OpenAlexafffundabout
Ege Kamber, Michael A. Busseri, Caitlin E. V. Mahy

Bibliographic record

VenueCognition · 2025
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsBrock UniversityYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEpisodic memoryConstructiveAutobiographical memoryChronesthesiaCognitionMagical thinkingNarrative

Abstract

fetched live from OpenAlex

Constructive episodic simulation, self-projection, and scene construction are three prominent cognitive mechanisms posited to underpin episodic thinking. This study investigated whether these mechanisms explain individual differences in children's episodic thinking and relations to other related abilities during middle childhood (i.e., imagination, perspective-taking, and spatial navigation). A sample of 150 Canadian children aged 8 to 10 years completed the Autobiographical Interview, in which they described future, past, current, and make-believe events, and several other behavioural tasks measuring their perspective-taking, spatial navigation, narrative ability, and receptive vocabulary. Structural equation modelling revealed significant covariance among episodic thinking for future, past, and current events, imagination, and spatial navigation, but not perspective-taking. When children's verbosity was controlled, these relations were weakened in magnitude and spatial navigation was no longer significantly related to episodic thinking processes. These results support constructive episodic simulation and scene construction accounts, as well as a more general underlying episodic simulation ability, as mechanisms for episodic thinking in middle childhood.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.464
Threshold uncertainty score0.460

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.012
GPT teacher head0.271
Teacher spread0.259 · 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.

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

Citations0
Published2025
Admission routes3
Has abstractyes

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