Neuronal activity in superior colliculus signals both stimulus identity and saccade goals during visual conjunction search
Bibliographic record
Abstract
Although we know that the process of saccade target selection is reflected in the activity of sensory-motor neurons within saccade executive centers, the description of this process at the neural level has yet to fully account for all selection outcomes. The current study sought to determine how neuronal activity in the intermediate layers of the superior colliculus (SC) determines correct saccade target selection by examining the activity of visuomovement neurons during both correct and error trials of monkeys performing a relatively difficult visual conjunction search task. We found that a stimulus presented in a neuron's response field, but not foveated, was associated with greater activity if it was the search target instead of a distractor, indicating that SC neurons could represent stimulus identity. Nevertheless, activity was greater when a saccade was made to a stimulus than when it was not, further implicating these neurons in selecting the saccade goal. Together with the related observation that, when the target fell in their response fields, SC neurons discharged significantly more if the monkey correctly selected it instead of a distractor, these results suggest that visual stimuli are selected when these neurons reach a critical activation level. Our findings show that the outcome of all visual search trials, regardless of the stimulus being selected, is predicted by SC neuronal activity.
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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".