Mnemonic prediction errors bias hippocampal states
Bibliographic record
Abstract
Abstract In situations when our experience violates our predictions, it is adaptive to upregulate encoding of novel information, while down-weighting retrieval of erroneous memory predictions to promote an updated representation of the world. We asked whether mnemonic prediction errors promote distinct hippocampal processing ‘states’ by leveraging recent results showing that encoding and retrieval processes are supported by distinct patterns of connectivity, or ‘states’, across hippocampal subfields. During fMRI scanning, participants were cued to retrieve well-learned room-images and were then presented with either an image identical to the learned room or a modified version (1-4 changes). We found that CA1-entorhinal connectivity increased, and CA1-CA3 connectivity decreased, with the number of changes to the learned rooms. Further, stronger memory predictions measured in CA1 during the cue correlated with the CA1-entorhinal connectivity increase in response to violations. Our findings provide a mechanism by which mnemonic prediction errors may drive memory updating - by biasing hippocampal states.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".