Competitive Integration of Visual and Preparatory Signals in the Superior Colliculus during Saccadic Programming
Bibliographic record
Abstract
Efficient behavior requires that internally specified motor plans be integrated with incoming sensory information. Motor preparation and visual signals converge in the intermediate and deep layers of the superior colliculus (SC) to influence saccade planning and execution; however, the mechanism by which these sometimes conflicting signals are combined remains unclear. We studied this issue by presenting visual distractors as monkeys prepared saccades toward an upcoming target whose timing and location were fully predictable. Monkeys made more distractor-directed errors when the spatial location of visual distractors more closely coincided with the saccadic goal. Concomitant pretarget activity of SC visuomotor neurons, whose response fields were centered on the saccadic goal, was similarly increased by the presentation of nearby distractors and inhibited by the presentation of distant distractors. Finally, subthreshold microstimulation of the SC shifted the pattern of distractor-directed errors away from the saccadic goal toward that specified by the site of stimulation. Together, our results suggest that the likelihood of saccade generation is influenced by the spatial register of internal motor preparation signals and external sensory signals across the topographically organized SC map.
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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) | 0.000 | 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".