Goal-Dependent Modulation of Fast Feedback Responses in Primary Motor Cortex
Bibliographic record
Abstract
Many human studies have demonstrated that rapid motor responses (i.e., muscle-stretch reflexes) to mechanical perturbations can be modified by a participant's intended response. Here, we used a novel experimental paradigm to investigate the neural mechanisms that underlie such goal-dependent modulation. Two monkeys positioned their hand in a central area against a constant load and responded to mechanical perturbations by quickly placing their hand into visually defined spatial targets. The perturbation was chosen to excite a particular proximal arm muscle or isolated neuron in primary motor cortex and two targets were placed so that the hand was pushed away from one target (OUT target) and toward the other (IN target). We chose these targets because they produced behavioral responses analogous to the classical verbal instructions used in human studies. A third centrally located target was used to examine responses with a constant goal. Arm muscles and neurons robustly responded to the perturbation and showed clear goal-dependent responses ∼35 and 70 ms after perturbation onset, respectively. Most M1 neurons and all muscles displayed larger perturbation-related responses for the OUT target than the IN target. However, a substantial number of M1 neurons showed more complex patterns of target-dependent modulation not seen in muscles, including greater activity for the IN target than the OUT target, and changes in target preference over time. Together, our results reveal complex goal-dependent modulation of fast feedback responses in M1 that are present early enough to account for goal-dependent stretch responses in arm muscles.
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 | 0.000 |
| 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".