Dynamic hippocampal-cortical interactions during event boundaries support retention of complex narrative events
Bibliographic record
Abstract
Abstract According to most memory theories, encoding involves continuous communication between the hippocampus and neocortex leaving the temporal dynamics of hippocampal-neocortical interactions often overlooked. Recent work has shown that we perceive complex events in our lives as dynamic, with relatively distinct starting and stopping points known as event boundaries. Event boundaries may be important for memory, as they are associated with increased activity in the hippocampus, and extended neocortical regions (the posterior cingulate cortex, lateral parietal cortex, and parahippocampal cortex). Our objective was to determine how functional connectivity between the hippocampus and neocortical regions during the encoding of naturalistic events (movies) related to subsequent retrieval and retention of those events. Participants encoded two 16-minute cartoon movies during fMRI scanning. After encoding, participants freely recalled one of the movies immediately, and the other after a 2-day delay. We quantified hippocampal-neocortical functional connectivity (FC) at time windows around each event onset, middle, and offset, and compared these FC measures with subsequent recall. These analyses revealed that higher FC between the hippocampus and the posterior medial network (PMN) at an event’s offset related to whether that event was subsequently recalled. In contrast, mid-event connectivity between the hippocampus and PMN was associated with poorer memory. Furthermore, hippocampal-PMN offset connectivity predicted not only whether events were retained in memory, but also the degree to which these events could be recalled in detail after a 2-day delay. These data demonstrate that the relationship between memory encoding and hippocampal-neocortical interaction is more dynamic than suggested by most memory theories, and they converge with recent modeling work suggesting that event offset is an optimal time for encoding.
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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".