Decoding motor imagery and action planning in the early visual cortex: Overlapping but distinct neural mechanisms
Bibliographic record
Abstract
Recent evidence points to a role of the primary visual cortex that goes beyond visual processing into high-level cognitive and motor-related functions, including action planning, even in absence of feedforward visual information. It has been proposed that, at the neural level, motor imagery is a simulation based on motor representations, and neuroimaging studies have shown overlapping and shared activity patterns for motor imagery and action execution in frontal and parietal cortices. Yet, the role of the early visual cortex in motor imagery remains unclear. Here we used multivoxel pattern analyses on functional magnetic resonance imaging (fMRI) data to examine whether the content of motor imagery and action intention can be reliably decoded from the activity patterns in the retinotopic location of the target object in the early visual cortex. Further, we investigated whether the discrimination between specific actions generalizes across imagined and intended movements. Eighteen right-handed human participants (11 females) imagined or performed delayed hand actions towards a centrally located object composed of a small shape attached on a large shape. Actions consisted of grasping the large or small shape, and reaching to the center of the object. We found that despite comparable fMRI signal amplitude for different planned and imagined movements, activity patterns in the early visual cortex, as well as dorsal premotor and anterior intraparietal cortex, accurately represented action plans and action imagery. However, movement content is similar irrespective of whether actions are actively planned or covertly imagined in parietal but not early visual or premotor cortex, suggesting a generalized motor representation only in regions that are highly specialized in object directed grasping actions and movement goals. In sum, action planning and imagery have overlapping but non identical neural mechanisms in the cortical action network.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".