Bibliographic record
Abstract
The basal ganglia (BG) have been considered as a key structure for volitional action preparation. Neurons in the striatum, the main BG input stage, increase activity gradually before volitional action initiation. However, because of the diversity of striatal motor commands, such as automatic (sensory driven) and volitional (internally driven) actions, it is still unclear whether an appropriate set of neurons encoding volitional actions are activated selectively for volitional action preparation. Here, using the antisaccade paradigm (look away from a visual stimulus), we dissociated neurons in the caudate nucleus, the oculomotor striatum, encoding predominantly automatic saccades toward the stimulus and volitional saccades in the opposite direction of the stimulus in monkeys. We found that before actual saccade directions were defined by visual stimulus appearance, neurons encoding volitional saccades increased activity with elapsed time from fixation initiation and by a temporal gap between fixation point disappearance and stimulus appearance. Their activity was further enhanced by an antisaccade instruction and correlated with antisaccade behavior. Neurons encoding automatic saccades also increased activity with elapsed time from fixation initiation and by a fixation gap. However, the activity of this type of neuron was not enhanced by an antisaccade instruction nor correlated with antisaccade behavior. We conclude that caudate neurons integrate nonspatial signals, such as elapsed time from fixation initiation, fixation gap, and task instructions, to preset BG circuits in favor of volitional actions to compete against automatic actions even before automatic and volitional commands are programmed with spatial information.
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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 | 0.000 |
| 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 | 0.001 |
| 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".