Episodic autobiographical memory is associated with variation in the size of hippocampal subregions
Bibliographic record
Abstract
Abstract Striking individual differences exist in the human capacity to recollect past events, yet, little is known about the neural correlates of such individual differences. Studies investigating hippocampal volume in relation to individual differences in laboratory measures of episodic memory in young adults suggest that whole hippocampal volume is unrelated (or even negatively associated) with episodic memory. However, anatomical and functional specialization across hippocampal subregions suggests that individual differences in episodic memory may be linked to particular hippocampal subregions, as opposed to whole hippocampal volume. Given that the DG/CA2/3 circuitry is thought to be especially critical for supporting episodic memory in humans, we predicted that the volume of this region would be associated with individual variability in episodic memory. This prediction was supported using high‐resolution MRI of the hippocampal subfields and measures of real‐world (autobiographical) episodic memory. In addition to the association with DG/CA2/3, we further observed a relationship between episodic autobiographical memory and subiculum volume, whereas no association was observed with CA1 or with whole hippocampal volume. These findings provide insight into the possible neural substrates that mediate individual differences in real‐world episodic remembering in humans.
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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 | 0.000 | 0.000 |
| 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".