Examining Temporal Memory and Flexible Retrieval of Conventional Time Knowledge across Middle to Late Childhood
Bibliographic record
Abstract
Memory for the time associated with past events is critical for our understanding of episodic memory and its development. Relatively little is known about the factors that influence temporal memory development. One such factor examined in the literature is semantic knowledge for time (conventional time knowledge; CTK). Other possible factors include domain general skills (e.g., working memory). The goals of this study were to a) assess temporal memory for past events in middle to late childhood using a naturalistic, yet controlled task, b) examine the relation between temporal memory performance and CTK, c) examine the factors that support the development of conventional time knowledge, and d) test which factors best predict temporal memory performance. Participants included 7-year-olds, 9-year-olds, 11-year-olds and young adults (N = 140). They engaged in naturalistic events in unique locations in the lab over a span of 2–3 hours. One week later, participants were asked to place the events on an arbitrary timeline, and we measured deviations from the precise time that each event took place. Performance on the CTK task, but not age, contributed unique variance to accuracy in the timeline task, replicating findings from previous work. Further, vocabulary and working memory but not inhibitory control or age, were unique predictors of performance on the CTK task. Finally, vocabulary surpassed CTK task performance as a significant predictor of temporal memory. The implications of this work to our understanding of temporal memory, semantic knowledge for time and episodic memory development are discussed.
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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 | 0.003 |
| 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 | 0.001 |
| 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".