Human parietal cortex lesions impact the precision of spatial working memory
Bibliographic record
Abstract
The neural mechanisms that support working memory (WM) depend on persistent neural activity. Within topographically organized maps of space in dorsal parietal cortex, spatially selective neural activity persists during WM for location. However, to date, the necessity of these topographic subregions of human parietal cortex for WM remains unknown. To test the causal relationship of these areas to WM, we compared the performance of patients with lesions to topographically organized parietal cortex with those of controls on a memory-guided saccade (MGS) task as well as a visually guided saccade (VGS) task. The MGS task allowed us to measure WM precision continuously with great sensitivity, whereas the VGS task allowed us to control for any deficits in general spatial or visuomotor processing. Compared with controls, patients generated memory-guided saccades that were significantly slower and less accurate, whereas visually guided saccades were unaffected. These results provide key missing evidence for the causal role of topographic areas in human parietal cortex for WM, as well as the neural mechanisms supporting WM.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".