Inactivation of the dorsal hippocampus does not affect learning during exploration of a novel environment
Bibliographic record
Abstract
The conditioned cue preference (CCP) task was used to study the ability of rats to discriminate between spatial locations. Food-deprived rats explored an eight-arm radial maze with no food present (pre-exposure). On subsequent days, they were alternately confined in one arm of the maze with food and in another arm with no food (training), followed by a preference test with no food present, to determine if they had learned to discriminate between the two arm locations. No injections were given during the two latter phases. With adjacent radial maze arms, rats given three 10-min pre-exposure sessions and four food-pairing trials exhibited a preference for their food-paired arms; rats not pre-exposed did not exhibit this preference. Rats pre-exposed 30 min after dorsal hippocampus injections of muscimol exhibited the preference. With widely separated maze arms, rats given two training trials with no pre-exposure exhibited a preference for the food-paired arm; rats that were given one pre-exposure session did not. Rats pre-exposed 30 min after dorsal hippocampus injections of muscimol did not exhibit the preference. The same intrahippocampal muscimol injections that failed to affect the influence of pre-exposure on CCP learning with both arm configurations impaired win-shift performance, a standard test of spatial learning. These findings suggest that a functional dorsal hippocampus is not required for the (incidental or latent) learning that occurs during unreinforced exploration of a novel environment. The information acquired during this activity subsequently produces a latent learning effect if it is used to discriminate between two ambiguous locations (adjacent arms) or a latent inhibition--like effect if it is used to discriminate between two unambiguous locations (separated maze arms).
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".