Causal roles of frontoparietal cortical areas in feedback control of the limb
Bibliographic record
Abstract
Summary Goal-directed motor corrections are surprisingly fast and complex, but little is known on how they are generated by the central nervous system. Here we show that temporary cooling of dorsal premotor cortex (PMd) or parietal area 5 (A5) in behaving monkeys caused impairments in corrective responses to mechanical perturbations of the forelimb. Deactivation of PMd impaired both spatial accuracy and response speed, whereas deactivation of A5 impaired spatial accuracy, but not response speed. Simulations based on optimal feedback control demonstrated that ‘deactivation’ of the control policy (reduction of feedback gain) impaired both spatial accuracy and response speed, whereas ‘deactivation’ in state estimation (reduction of Kalman gain) impaired spatial accuracy but not response speed, paralleling the impairments observed from deactivation of PMd and A5, respectively. Furthermore, combined deactivation of both cortical regions led to additive impairments of individual deactivations, whereas reducing the amount of cooling (i.e. milder cooling) to PMd led to impairments in response speed, but not spatial accuracy, both also predicted by the model simulations. These results provide causal support that higher order motor and somatosensory regions beyond primary somatosensory and primary motor cortex are involved in generating goal-directed motor responses. As well, the computational models suggest that the distinct patterns of impairments associated with these cortical regions reflect their unique functional roles in goal-directed feedback control.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".