Constructive episodic simulation, self-projection, and scene construction: Investigating the mechanisms of children's episodic thinking
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".