Lesions of Area 5 of the Posterior Parietal Cortex in the Cat Produce Errors in the Accuracy of Paw Placement During Visually Guided Locomotion
Bibliographic record
Abstract
We developed a novel locomotor task in which cats step over obstacles that move at a different speed from that of the treadmill on which the cat is walking: we refer to this as a visual dissociation locomotion task. Slowing the speed of the obstacle with respect to that of the treadmill sometimes led to a major change in strategy so that cats made two steps with the hindlimbs before stepping over the obstacle (double step strategy) instead of the single step (standard strategy) observed when the obstacle was at the same speed as the treadmill. In addition, in the step preceding the step over the obstacle, the paws were placed significantly closer to the obstacle in the visual dissociation task than when the treadmill and the obstacle were at the same speed. After unilateral lesion of area 5 of the posterior parietal cortex (PPC), the cats frequently hit the obstacle as they stepped over it, especially in the visual dissociation task. This locomotor deficit was linked to significant differences in the location in which the forelimbs were placed in the step preceding that over the obstacle compared with the prelesion control. Cats also frequently hit the obstacle with their hindlimbs even when the forelimbs negotiated the obstacle successfully; this suggests an important role for the posterior parietal cortex in the coordination of the forelimbs and hindlimbs. Together, these results suggest an important contribution of the PPC to the planning of visually guided gait modifications.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".