Processing idiothetic cues to remember visited locations: Hippocampal and vestibular contributions to radial‐arm maze performance
Bibliographic record
Abstract
This research examined whether rats can use idiothetic cues to form spatial memories in the radial-arm maze (RM) and whether the hippocampus is involved in such ability. A possible contribution of the vestibular system to RM performance was also investigated. Rats with excitotoxic hippocampal lesions and sham-operated controls were trained on two versions of the RM task. In the Light condition, a unique visual insert was apposed on each arm floor and rats could choose which arm to enter next by relying on visual and/or idiothetic stimuli. In the Dark condition, the task was administered in darkness and success required processing of idiothetic cues to remember visited locations on the maze. In experiment 1, the performance of lesioned rats was impaired in the Light condition, but both control and lesioned rats learned to avoid already visited arms. In the Dark condition, the performance of controls improved over time whereas a severe deficit was observed in rats with hippocampal lesions. Thus, control rats, but not hippocampal lesioned rats, can form spatial memories by processing idiothetic inputs. Experiment 2 showed that vestibular lesions disrupt performance in both the Light and the Dark conditions and confirmed that rats use idiothetic information, especially vestibular cues, while navigating in the RM. Therefore, cues generated during locomotion play an important role in hippocampal-dependent spatial memory.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".