Contribution of cells in the posterior parietal cortex to the planning of visually guided locomotion in the cat: effects of temporary visual interruption
Bibliographic record
Abstract
In the present study, we determined whether cells in the posterior parietal cortex (PPC) may contribute to the planning of voluntary gait modifications in the absence of visual input. In two cats we recorded the responses of 41 neurons in layer V of the PPC that discharged in advance of the gait modification to a 900-ms interruption of visual information (visual occlusion). The cats continued to walk without interruption during the occlusion, which produced only minimal changes in step cycle duration and paw placement. Visual occlusion applied during the period of cell discharge was without significant effect on discharge frequency in 57% of cells. In the other cells, the visual occlusion produced either significant decreases (18%) or increases (21%) of discharge activity (in 1 cell there was both an increase and a decrease). The mean latency of the changes was 356 ms for decreases and 252 ms for increases. In most neurons, discharge frequency, when modified, returned to the same levels as during unoccluded locomotion when vision was restored. In some cells, there were significant changes in discharge activity after the restoration of vision; these were associated with corrections of gait. These results suggest that the PPC is more involved in the visuomotor transformations necessary to plan gait modifications than in continual sensory processing of visual information. We further propose that cells in the PPC contribute both to the planning of gait modifications on the basis of only intermittent visual sampling and to visually guided online corrections of gait.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".