Prediction Errors Disrupt Hippocampal Representations and Update Episodic Memories
Bibliographic record
Abstract
Abstract The brain supports adaptive behavior by generating predictions, learning from errors, and updating memories to incorporate new information. Prediction error, or surprise, triggers learning when reality contradicts expectations. Prior studies have shown that the hippocampus signals prediction errors, but the hypothesized link to memory updating has not been demonstrated. In a human fMRI study, we elicited mnemonic prediction errors by interrupting familiar narrative videos immediately before the expected endings. We found that prediction error reversed the relationship between univariate hippocampal activation and memory: greater hippocampal activation predicted memory preservation after expected endings, but memory updating after surprising endings. In contrast to previous studies, we showed that univariate activation was insufficient for understanding hippocampal prediction error signals. We explained this surprising finding by tracking both the evolution of hippocampal activation patterns and connectivity between the hippocampus and neuromodulatory regions. We found that hippocampal activation patterns stabilized as each narrative episode unfolded, suggesting sustained episodic representations. Prediction errors disrupted these sustained representations, and the degree of disruption predicted memory updating. The relationship between hippocampal activation and subsequent memory depended on concurrent basal forebrain activation, supporting the idea that cholinergic modulation regulates attention and memory. We conclude that prediction errors create conditions that favor memory updating, prompting the hippocampus to abandon ongoing predictions and make memories malleable. Significance Our brains draw on memories to predict the future; when our predictions are incorrect, we must update our memories to improve future predictions. Past studies have demonstrated that the hippocampus signals prediction error , or surprise, but have not linked this neural signal to memory updating. Here, we uncover this missing connection: We show that mnemonic prediction errors change the role of the hippocampus, reversing the relationship between hippocampal activation and memory outcomes. We examine the mechanisms of this shift in neural processing, showing that prediction errors disrupt the temporal continuity of hippocampal patterns. We propose that prediction errors disrupt sustained representations and enable memory updating. Our findings bear implications for improving education, understanding eyewitness memory distortion, and treating pathological memories.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".