Intrinsic medial temporal lobe connectivity relates to individual differences in episodic autobiographical remembering
Bibliographic record
Abstract
People vary in how they remember the past: some recall richly detailed episodes; others more readily access the semantic features of events. The neural correlates of such trait-like differences in episodic and semantic remembering are unknown. We found that self-reported individual differences in how one recalls the past were related to predictable intrinsic connectivity patterns of the medial temporal lobe (MTL) memory system. A pattern of MTL connectivity to posterior brain regions supporting visual-perceptual processing (occipital/parietal cortices) was related to the endorsement of episodic memory-based remembering (recalling spatiotemporal event information), whereas MTL connectivity to inferior and middle prefrontal cortical regions was related to the endorsement of semantic memory-based remembering (recalling facts). These findings suggest that the tendency to engage in episodic autobiographical remembering is associated with accessing and constructing detailed images of a past event in memory, while the tendency to engage in semantic autobiographical remembering is associated with organizing and integrating higher-order conceptual information. More broadly, these findings suggest that differences in how people naturally use memory are instantiated though distinct patterns of MTL functional connectivity.
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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 | 0.000 | 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) | 0.002 | 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".