Retrograde amnesia for visual memories after hippocampal damage in rats
Bibliographic record
Abstract
It is generally believed that the hippocampus is not required for simple discrimination learning. However, a small number of studies have shown that hippocampus damage impairs retention of a previously learned visual discrimination task. We propose that, although simple discrimination learning may proceed in the absence of the hippocampus, it plays an important role in this type of learning when it is intact. In order to test the role of the hippocampus in simple discrimination learning, we performed a series of experiments utilizing a two-choice picture discrimination task. Our experiments confirm that rats readily learn simple two-choice picture discriminations after hippocampus damage. However, if such discriminations are first learned while the hippocampus is intact, subsequent hippocampus damage causes severe retrograde amnesia for the discriminations. Furthermore, retrograde amnesia for simple picture discriminations was equally severe when the interval between training and damage was 1 d or 60 d; remote picture memories are not spared. Similarly, the rule or schema underlying a recently or remotely acquired picture discrimination learning set was lost after hippocampus damage. The severity of retrograde amnesia for simple picture discriminations is negatively correlated with the volume of spared hippocampus tissue. Thus, the hippocampus plays an essential role in long-term memories supporting simple picture discriminations.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".