Developmental differences in hippocampal long-axis contributions to memory precision
Bibliographic record
Abstract
Memory precision varies along the hippocampal long axis in the mature brain. Yet, little is known about how the development of long-axis functionality influences this precision. We characterized how children and adults engage the long axis to form precise memories. Children (7-9 years) and adults performed two tasks that encouraged an orientation to specific details versus general scene categories during encoding. After, they performed a recognition test with studied scenes and yoked lures. Adults had more precise memories than children in that they better discriminated studied scenes from lures. Yet, both groups showed memory benefits after orienting to specifics. Examining hippocampal engagement revealed that the two age groups relied on different subregions during specific encoding, with children recruiting the posterior third and adults the anterior third. Engagement was also differently related to memory quality. While the posterior third supported subsequent memory across age groups, both anterior and posterior thirds showed developmental differences in how they encouraged false memories-reflective of mnemonic breadth. Individual differences in the source of specific encoding along the long axis revealed that premature shifts towards an adult-like profile were disadvantageous to children's memory. Our results suggest as development unfolds, refinements to the functionality of the entire long axis supports memory imprecision.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".