Premotor Correlates of Integrated Feedback Control for Eye–Head Gaze Shifts
Bibliographic record
Abstract
Simple activities like picking up the morning newspaper or catching a ball require finely coordinated movements of multiple body segments. How our brain readily achieves such kinematically complex yet remarkably precise multijoint movements remains a fundamental and unresolved question in neuroscience. Many prevailing theoretical frameworks ensure multijoint coordination by means of integrative feedback control. However, to date, it has proven both technically and conceptually difficult to determine whether the activity of motor circuits is consistent with integrated feedback coding. Here, we tested this proposal using coordinated eye-head gaze shifts as an example behavior. Individual neurons in the premotor network that command saccadic eye movements were recorded in monkeys trained to make voluntary eye-head gaze shifts. Head-movement feedback was experimentally controlled by unexpectedly and transiently altering the head trajectory midflight during a subset of movements. We found that the duration and dynamics of neuronal responses were appropriately updated following head perturbations to preserve global movement accuracy. Perturbation-induced increases in gaze shift durations were accompanied by equivalent changes in response durations so that neuronal activity remained tightly synchronized to gaze shift offset. In addition, the saccadic command signal was updated on-line in response to head perturbations applied during gaze shifts. Nearly instantaneous updating of responses, coupled with longer latency changes in overall discharge durations, indicated the convergence of at least two levels of feedback. We propose that this strategy is likely to have analogs in other motor systems and provides the flexibility required for fine-tuning goal-directed movements.
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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) | 0.001 | 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".