Differential attentional demands on implicit and explicit associative memory in children 8–12 years old
Bibliographic record
Abstract
Associative memory improves during childhood, suggesting an age-related improvement in the binding mechanism responsible for linking information together. However, tasks designed to measure associative memory not only measure binding, but also place demands on attention. This makes it difficult to dissociate age-related improvements in memory from the development of attention. One way to reduce attentional demands is to test memory implicitly versus explicitly. In this study, children (8-, 10-, and 12-years-old) completed separate implicit and explicit associative memory tests. For the implicit task, children incidentally encoded pairs of objects by making an object categorization decision. At test, they completed the same task, but unbeknownst to the participants, the pairs were either intact, rearranged, or new. Next, children completed another incidental encoding phase, then an explicit test in which they indicated whether the pairs were intact, rearranged, or new. For the implicit test, all age groups had faster reaction times for intact than rearranged pairs (indicative of implicit associative memory). In the explicit test, memory performance (d’) improved with age. A separate measure of attention related to performance in both the explicit and implicit tasks. Together, these results support that attentional mechanisms contribute to age-related improvements in associative memory.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".