The development of children’s prospective memory: Lessons for developmental science
Bibliographic record
Abstract
Abstract The study of children’s prospective memory has gained new momentum over the past 20 years and is now an active area of research in cognitive development. Yet, this resurgence has been accompanied by significant challenges that offer important lessons and insights for other areas of developmental science. In this article, I provide an overview and theoretical accounts of the development of children’s prospective memory. I then describe three puzzling findings that remain unresolved in children’s development of this kind of memory: (1) the contradictory effects of delay length on prospective memory performance, (2) why reminders sometimes fail to improve prospective memory performance, and (3) why parent reports and behavioral measures of prospective memory might fail to correlate. I discuss how each challenge can be applied broadly to other areas of children’s development. I conclude by highlighting challenges and opportunities as the field moves forward.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".