Differential reach vector computations in mIPS and PMd as revealed through HD-tDCS
Bibliographic record
Abstract
Abstract Recent neural and behavioural findings provide support that the medial intraparietal sulcus (mIPS) and dorsal premotor (PMd) activity reflect aspects of a kinematic plan for reaching movements. However, it is unclear how these two regions differentially contribute to reach planning. Here, we used high-definition transcranial direct current stimulation (HD-tDCS; 4 x 1 electrode placement; 2 mA for 20 min; 3 cm radius) to investigate the functional roles of mIPS and PMd in the left hemisphere of humans. We examined the changes in endpoint error in reaching task with different initial hand positions and different target locations spanning both visual hemi-fields. Participants completed the task with (stimulation, post-stimulation) and without stimulation (pre-stimulation) of individually fMRI-localized cortical areas mIPS and PMd. We found a significant interaction effect between initial hand position (IHP) and target position on the difference in horizontal endpoint error after cathodal left mIPS stimulation and significant IHP and target position main effects after cathodal left PMd stimulation, suggesting that IHP and target position are not yet integrated into a movement vector at the input of the mIPS, but are integrated in the input of PMd. Hence, these findings reveal a distinction between mIPS and PMd in the stages of movement vector formation for reaching movements and indicate that HD-tDCS is a viable method for perturbing localized cortical activity to elucidate localized cortical function.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".