Anterior Hippocampal Engagement during Memory Formation Predicts Subsequent False Recognition of Similar Experiences
Bibliographic record
Abstract
People better remember experiences when they orient to meaning over surface-level perceptual features. Such an orientation-related memory boost has been associated with engagement of both hippocampus (HPC) and neocortex during encoding. However, less is known about the neural mechanisms by which a cognitive orientation toward meaning might also promote memory errors, with one open question being whether the HPC-a region traditionally implicated in precise memory formation-also contributes to behavioral imprecision. We used fMRI to characterize encoding-phase signatures as people oriented toward the meaning (story) versus perceptual style (artist) of storybook-style illustrations and then linked them to subsequent true and false memories. We found that story and artist orientation tasks were each associated with both unique univariate profiles and consistent neural states defined using multivoxel patterns. Linking these neural signatures to behavior, we found that greater medial pFC activation and alignment of neural patterns to the story (but not artist) state was related to subsequent memory success on a trial-by-trial basis. Moreover, among successfully remembered experiences, greater anterior HPC engagement at encoding was associated with a higher likelihood of related false recognitions, consistent with the encoding of broad traces in this region. Interestingly, these effects did not reliably vary by cued orientation. These results suggest that, irrespective of the cued encoding orientation, neocortical and hippocampal mechanisms associated with orienting to meaning (story) over perceptual (artist) features may support memory, with the formation of generalizable memories being a specialty of anterior HPC.
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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 | 0.001 | 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".