From vision to memory: How scene-sensitive regions support episodic memory formation during child development
Bibliographic record
Abstract
Previous brain imaging studies have identified three brain regions that selectively respond to visual scenes, the parahippocampal place area (PPA), the occipital place area (OPA), and the retrosplenial cortex (RSC). There is growing evidence that these visual scene-sensitive regions process different types of scene information and may have different developmental timelines in supporting scene perception. How these scene-sensitive regions support memory functions during child development is largely unknown. We investigated PPA, OPA and RSC activations associated with episodic memory formation in childhood (5-7 years of age) and young adulthood, using a subsequent scene memory paradigm and a functional localizer for scenes. PPA, OPA, and RSC subsequent memory activation and functional connectivity differed between children and adults. Subsequent memory effects were found in activations of all three scene regions in adults. In children, however, robust subsequent memory effects were only found in the PPA. Functional connectivity during successful encoding was significant among the three regions in adults, but not in children. PPA subsequently memory activations and PPA-RSC subsequent memory functional connectivity correlated with accuracy in adults, but not children. These age-related differences add new evidence linking protracted development of the scene-sensitive regions to the protracted development of episodic 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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".