Concurrent feature-specific reactivation within the hippocampus and neocortex facilitates episodic memory retrieval
Bibliographic record
Abstract
Abstract The hippocampus is a key brain region for the storage and retrieval of episodic memories, but how it performs this function is unresolved. According to the hippocampal indexing theory, the hippocampus stores an event-specific index of the pattern of neocortical activity that occurred during perception. During retrieval, reactivation of the index by a partial cue facilitates the reactivation of the associated neocortical pattern. Therefore, event-specific retrieval requires joint reactivation of the hippocampal index and the associated neocortical networks. To test this theory, we examine the relation between performance on a recognition memory task requiring retrieval of image-specific visual details and feature-specific reactivation within the hippocampus and neocortex. We show that trial-by-trial recognition accuracy correlates with neural reactivation of low-level features (e.g. luminosity and edges) within the posterior hippocampus and early visual cortex for participants with high recognition lure accuracy. As predicted, the two regions interact, such that recognition accuracy correlates with hippocampal reactivation only when reactivation co-occurs within the early visual cortex (and vice-versa). In addition to supporting the hippocampal indexing theory, our findings show large individual differences in the features underlying visual memory and suggest that the anterior and posterior hippocampus represents gist-like and detailed features, respectively.
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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".