Temporal Evolution of Target Representation, Movement Direction Planning, and Reach Execution in Occipital–Parietal–Frontal Cortex: An fMRI Study
Bibliographic record
Abstract
The cortical mechanisms for reach have been studied extensively, but directionally selective mechanisms for visuospatial target memory, movement planning, and movement execution have not been clearly differentiated in the human. We used an event-related fMRI design with a visuospatial memory delay, followed by a pro-/anti-reach instruction, a planning delay, and finally a "go" instruction for movement. This sequence yielded temporally separable preparatory responses that expanded from modest parieto-frontal activation for visual target memory to broad occipital-parietal-frontal activation during planning and execution. Using the pro/anti instruction to differentiate visual and motor directional selectivity during planning, we found that one occipital area showed contralateral "visual" selectivity, whereas a broad constellation of left hemisphere occipital, parietal, and frontal areas showed contralateral "movement" selectivity. Temporal analysis of these areas through the entire memory-planning sequence revealed early visual selectivity in most areas, followed by movement selectivity in most areas, with all areas showing a stereotypical visuo-movement transition. Cross-correlation of these spatial parameters through time revealed separate spatiotemporally correlated modules for visual input, motor output, and visuo-movement transformations that spanned occipital, parietal, and frontal cortex. These results demonstrate a highly distributed occipital-parietal-frontal reach network involved in the transformation of retrospective sensory information into prospective movement plans.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".