Memory Overlap Enhances Shared Feature Recognition but Hinders Specific Memory in Adolescents and Adults
Bibliographic record
Abstract
Over time we accumulate memories for many related experiences. However, it remains poorly understood how this relatedness, or overlap, among learned information shapes how we remember shared and unique features. The current study investigated this question and further asked whether effects of overlap on memory differ in adolescence compared to adulthood, given evidence that memory specificity continues to be refined beyond childhood. We had adolescents (12-13 years old) and adults learn pairs of objects that overlapped with one another to different degrees and then tested their memory for both overlapping and pair-unique features. Across both age groups, we found that greater overlap boosted memory for the overlapping feature but also led to worse memory for unique features. Further, adolescents were more detrimentally affected by high overlap than adults when recalling specific pairs. Our results suggest there may be a trade-off between memory for shared and unique features of overlapping materials and that adolescents experience a greater cost to this trade-off. More generally, we find that the connections among learned information play an important role in how it is remembered.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".