Effector selection precedes reach planning in the dorsal parietofrontal cortex
Bibliographic record
Abstract
Experimental evidence and computational modeling suggest that target selection for reaching is associated with the parallel encoding of multiple movement plans in the dorsomedial posterior parietal cortex (dmPPC) and the caudal part of the dorsal premotor cortex (PMdc). We tested the hypothesis that a similar mechanism also accounts for arm selection for unimanual reaching, with simultaneous and separate motor goal representations for the left and right arms existing in the right and left parietofrontal cortex, respectively. We recorded simultaneous electroencephalograms and functional MRI and studied a condition in which subjects had to select the appropriate arm for reaching based on the color of an appearing visuospatial target, contrasting it to a condition in which they had full knowledge of the arm to be used before target onset. We showed that irrespective of whether subjects had to select the arm or not, activity in dmPPC and PMdc was only observed contralateral to the reaching arm after target onset. Furthermore, the latency of activation in these regions was significantly delayed when arm selection had to be achieved during movement planning. Together, these results demonstrate that effector selection is not achieved through the simultaneous specification of motor goals tied to the two arms in bilateral parietofrontal cortex, but suggest that a motor goal is formed in these regions only after an arm is selected for action.
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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 | 0.001 | 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".