Bibliographic record
Abstract
Primary motor cortex (MI) plays an important role in voluntary motor behaviour, yet considerable debate remains on how neural processing within this brain region contributes to motor function. This article provides a brief review of the dominant conceptual frameworks used to interpret MI activity, notably servo-control during the 1970s and early 1980s, and sensorimotor transformations since that time. The former emphasized the use of feedback, but was abandoned because delays in sensory feedback could not permit sufficient feedback gains to generate observed patterns of limb movement. The latter framework focuses attention on identifying what coordinate frames, or representations, best describe neural processing in MI. However, studies have shown that MI activity correlates with a broad range of parameters of motor performance from spatial target location, hand or joint motion, joint torque and muscle activation patterns. Further, these representations can change across behaviours, such as from posture to movement. What do heterogeneous, labile neural representations mean and how do they help us understand how MI is involved in volitional motor control? Perhaps what is required is a new conceptual framework that re-focuses the experimental problem back on processes of control. Specifically, optimal feedback control has been proposed as a theory of the volitional motor system and it is argued here that it provides a rich, new perspective for addressing the role of MI and other brain regions in volitional motor control.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".