Neuromagnetic signatures of the spatiotemporal transformation for manual pointing
Bibliographic record
Abstract
Abstract Movement planning involves transforming the sensory goal representation into a command in motor coordinates. Surprisingly, the real-time dynamics of sensorimotor transformations at the whole brain level remain unknown, in part due to the spatiotemporal limitations of fMRI and neurophysiological recordings. Here, we used magnetoencephalography (MEG) during pro-/anti-wrist pointing to determine (1) the cortical areas involved in transforming visual signals into appropriate hand motor commands, and (2) how this transformation occurs in real time, both within and across the regions involved. We computed sensory, motor, and sensorimotor indices in 16 bilateral brain regions for direction coding based on hemispherically lateralized de/synchronization in the α (7-15Hz) and β (15-35Hz) bands. We found a visuomotor progression, from pure sensory codes in ‘early’ occipital-parietal areas, to a temporal transition from sensory to motor coding in the majority of parietal-frontal sensorimotor areas, to a pure motor code, in both the α and β bands. Further, the timing of these transformations revealed a top-down pro/anti cue influence that propagated ‘backwards’ from frontal through posterior cortical areas. These data directly demonstrate a progressive, real-time transformation both within and across the entire occipital-parietal-frontal network that follows specific rules of spatial distribution and temporal order.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".