Bibliographic record
Abstract
ABSTRACT The act of remembering can strengthen, but also distort memories. Parietal cortex is a candidate region involved in retrieval-induced memory changes given that it reflects retrieval success and represents retrieved content. Here, we conducted a human fMRI experiment to test whether different forms of reactivation in parietal cortex predict distinct consequences of memory retrieval. Subjects first studied associations between words and pictures of faces, scenes, or objects. Then, during ‘retrieval practice’, subjects repeatedly retrieved half of the previously learned pictures, reporting the vividness of the retrieved pictures. On the following day, subjects completed a recognition memory test for individual pictures. Critically, the recognition memory test included pictures that were highly similar to studied pictures (‘similar lures’). Behavioral results indicated that retrieval practice increased both the hit rate and false alarm rate to similar lures, confirming a causal influence of retrieval practice on subsequent memory. Using pattern similarity analyses, we measured two different levels of reactivation during retrieval practice: 1) generic ‘category-level’ reactivation and 2) idiosyncratic ‘item-level’ reactivation. Vivid remembering during retrieval practice was associated with stronger category- and item-level reactivation in parietal cortex. However, these measures differentially predicted performance on the subsequent recognition memory test: whereas higher category-level reactivation tended to predict false alarms to lures, item-level reactivation predicted correct rejections. These findings indicate that parietal reactivation can be decomposed to tease apart distinct consequences of memory retrieval.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".