Contributions of Parietal Cortex to the Working Memory of an Obstacle Acquired Visually or Tactilely in the Locomoting Cat
Bibliographic record
Abstract
A working memory of obstacles is essential for navigating complex, cluttered terrain. In quadrupeds, it has been proposed that parietal cortical areas related to movement planning and working memory may be important for guiding the hindlegs over an obstacle previously cleared by the forelegs. To test this hypothesis, parietal areas 5 and 7 were reversibly deactivated in walking cats. The working memory of an obstacle was assessed in both a visually dependent and tactilely dependent paradigm. Reversible bilateral deactivation of area 5, but not area 7, altered hindleg stepping in a manner indicating that the animals did not recall the obstacle over which their forelegs had stepped. Similar deficits were observed when area 5 deactivation was restricted to the delay during which obstacle memory must be maintained. Furthermore, partial memory recovery observed when area 5 function was deactivated and restored within this maintenance period suggests that the deactivation may suppress, but not eliminate, the working memory of an obstacle. As area 5 deactivations incurred similar memory deficits in both visual and tactile obstacle working memory paradigms, parietal area 5 is critical for maintaining the working memory of an obstacle acquired via vision or touch that is used to modify stepping for avoidance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".