Hippocampal damage and anterograde object-recognition in rats after long retention intervals
Bibliographic record
Abstract
Although several studies in rats have found that hippocampal damage has negligible effects on anterograde object-recognition memory, the findings are not entirely conclusive, because most studies have used retention intervals lasting only a few hours. We assessed the effects of neurotoxic hippocampal lesions on anterograde object recognition, using a novel-object preference test, with retention intervals that were considerably longer than in previous studies-24 h, 1 week, and 3 weeks. To promote object recognition after such long intervals, rats were familiarized with a sample object in an open field for 5 min/day for 5 consecutive days. Recognition was assessed by comparing the amount of time spent investigating the sample versus a novel object on a preference test at one of the postlearning intervals. The rats with hippocampal lesions displayed a normal novelty preference after a 3-week interval, and their performance across the three delay conditions was not significantly different from that of control rats. The findings indicate that extensive hippocampal damage spares anterograde object recognition in rats, even after retention intervals lasting days or weeks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".