A limited role for the hippocampus in the modulation of novel-object preference by contextual cues
Bibliographic record
Abstract
Recent evidence suggests that rats require an intact hippocampus in order to recognize familiar objects when they encounter them again in a different context. The two experiments reported here further examined how changes in context affect rats' performance on the novel-object preference (NOP) test of object-recognition memory, and how those effects interact with the effects of HPC damage. Rats with HPC lesions and control rats received NOP testing in either the same context in which they had previously encountered sample objects, or in a different but equally familiar context. In Experiment 1, the two contexts had very few overlapping cues within or outside the apparatus; thus, the differences between them were global. Consistent with previous results, control rats showed a novel-object preference in both the unchanged and (globally) changed contexts, whereas rats with HPC lesions displayed a preference only in the unchanged context. In Experiment 2, the context shift included only local features proximal to the test objects. The main results were the reverse of Experiment 1--rats with HPC lesions displayed a novel-object preference in both the unchanged and (locally) changed contexts, whereas control rats displayed a preference only in the unchanged context. The findings are consistent with the view that HPC damage does not cause a general inability to recognize objects, nor an inability to encode or store a representation of the context in which the objects are encountered. They suggest instead that HPC damage impairs the ability to remember specific locations of familiar objects within a particular context.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".