The impact of anterior thalamic lesions on active and passive spatial learning in stimulus controlled environments: Geometric cues and pattern arrangement.
Bibliographic record
Abstract
The anterior thalamic nuclei are vital for many spatial tasks. To determine more precisely their role, the present study modified the conventional Morris watermaze task. In each of 3 experiments, rats were repeatedly placed on a submerged platform in 1 corner (the 'correct' corner) of either a rectangular pool (Experiment 1) or a square pool with walls of different appearances (Experiments 2 and 3). The rats were then released into the pool for a first test trial in the absence of the platform. In Experiment 1, normal rats distinguished the 2 sets of corners in the rectangular pool by their geometric properties, preferring the correct corner and its diagonally opposite partner. Anterior thalamic lesions severely impaired this discrimination. In Experiments 2 and 3, normal rats typically swam directly to the correct corner of the square pool on the first test trial. Rats with anterior thalamic lesions, however, often failed to initially select the correct corner, taking more time to reach that location. Nevertheless, the lesioned rats still showed a subsequent preference for the correct corner. The same lesioned rats also showed no deficits in Experiments 2 and 3 when subsequently trained to swim to the correct corner over repeated trials. The findings show how the anterior thalamic nuclei contribute to multiple aspects of spatial processing. These thalamic nuclei may be required to distinguish relative dimensions (Experiment 1) as well as translate the appearance of spatial cues when viewed for the first time from different perspectives (Experiments 2, 3).
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".