Hippocampal–Cortical Networks Predict Conceptual versus Perceptually Guided Narrative Memory
Bibliographic record
Abstract
Current theories of event memory propose distinct connections between the hippocampus and neocortical regions, particularly those within the default mode network (DMN) subsystems, to support processing different types of content in memory. It has been established that hippocampal connectivity supports integrating this disparate content into unified event memories, suggesting that changing the way that an event is described could change the underlying hippocampal neural network. To address this knowledge gap, we developed event narratives that described the same core story (e.g., grocery shopping) with identical central story details described with additional descriptive details that were conceptually or perceptually related to the story. Using fMRI, we established hippocampal connectivity patterns as a group of human participants ( N = 35, of any sex) encoded these narratives and then related these patterns to later memory for the narrative details. Consistent with prior work, we found that the conceptual narratives were associated with stronger anterior hippocampal connectivity to regions within the core and dorsomedial DMN subsystems, and a portion of this connectivity pattern predicted memory for the core story of the narrative. The perceptual narratives were selectively associated with anterior hippocampal connectivity to parietal and lateral temporal regions and regions outside the standard DMN, in relation to memory performance. These results provide new insights into hippocampal and DMN functional organization and how distinct neural components contribute differently to event 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 | 0.002 |
| 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 | 0.000 | 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".