CORTTICAL AREA 5 IS NECESSARY FOR LONG-LASTING MEMORIES OF OBSTACLES ENCOUNTERED BY WALKKING CATS
Bibliographic record
Abstract
Vision is used by walking animals to ensure stable foot placements and to avoid obstacles in their path. Visual input is not used directly when walking over normal terrain, however. Instead, walking animals look three or four steps ahead of their current position and use short-term memory to remember pertinent objects for use at the appropriate time. In cluttered terrain, humans may rely on increased visual input, guiding foot movement directly when needed. In quadrupeds, the hindlegs can never beguided by visual input directly, suggesting a specialized memory system mightbe used during walking in cluttered terrain. We have previously reported evidence in support of this hypothesis, showing that cats that have stepped over an object with their forelegs and stopped remember its position for much longer (for up to ten minutes) than if they are stopped prior to stepping over it. In this presentation, I present research that tests the hypothesis that neural signals related to the movement of the forelegs are essential for the generation of this unique visual memory. By placing small lesions in cortical area 5, we disrupted the normal integration of sensory and motor signals in the parietal cortex. Cats with these lesions lost the long-lasting memory of straddled objects, and remembered these objects for no longer than if they had not stepped over them. The results support our hypothesis that a uniquely long-lasting memory system, initiated by neural signals related to foreleg stepping, is responsible for guiding the hindlegs of walking cats over obstacles. Funding from Canadian Institutes of Health Research and Alberta Heritage Foundation for Medical Research gratefully acknowledged.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".