Between-systems memory interference during retrieval
Bibliographic record
Abstract
Context memories normally depend on the hippocampus (HPC) but, in the absence of the HPC, other memory systems are capable of acquiring and supporting these memories. This suggests that the HPC can interfere with other systems during memory acquisition. Here we ask whether the HPC can also interfere with the retrieval of a context memory that was independently acquired by a non-HPC system. Specifically, we assess whether the HPC can impair the retrieval of a contextual fear-conditioning memory that was acquired while the HPC was temporarily inactive. Rats were infused with the γ-aminobutyric acid (GABA)(A) receptor agonist muscimol in the dorsal and ventral HPC either before acquisition, retrieval, or prior to both acquisition and retrieval, consistent with the effects of permanent HPC lesions on contextual fear conditioning, if the HPC was inactive at the time of acquisition and retention memory was intact. Thus, non-HPC systems acquired and supported this memory in absence of the HPC. However, if the HPC was inactive during acquisition but active thereafter, rats displayed severe deficits during the retention test. Moreover, when the same rats received a second retention test but with the HPC inactive at this time, the memory was recovered, suggesting that removal of a form of interference allowed the memory to be expressed. Combined, these findings imply that the HPC competes and/or interferes with retrieval of a long-term memory that was established in non-HPC systems.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".