Functional dissociation between fornix and hippocampus in spatial conditional learning
Bibliographic record
Abstract
Do lesions of the fornix or the hippocampus impair the performance of spatial conditional associative learning tasks, and to what extent does damage to these brain structures result in comparable deficits in this type of spatial behavior? The available evidence is not clear. In the present study, rats with lesions of the fornix, hippocampus, and normal control animals were trained on two spatial-visual conditional learning tasks in which they had to form arbitrary associations between visual stimuli and the context in which these stimuli were embedded. In one condition, rats were required to choose stimulus X in place A and stimulus Y in place B, and there was no overlap in the contents of the two scenes. In the other condition, the animal approached the same scene from two different directions and had to select stimulus X when the scene was viewed from perspective A and to select stimulus Y when the scene was viewed from perspective B. Rats with fornix transection were able to learn both conditional tasks at a rate comparable to that of normal control animals, but rats with hippocampal damage were severely impaired under both conditions. The findings extend the range of tasks known to be sensitive to damage of the hippocampus. In addition, the results argue that the fornix is not necessary for the acquisition of certain spatial conditional learning tasks and that this brain structure cannot be used as an indicator of hippocampal dysfunction under all learning situations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".