Distinct sensory‐ and goal‐related signals underlie the gap effect in the superior colliculus
Bibliographic record
Abstract
The removal of a fixation point (FP) prior to the appearance of a saccade target (gap effect) influences pre-motor circuits and reduces saccadic reaction time (SRT). Saccade preparation signals underlying the gap effect have been observed within the intermediate layers of the superior colliculus (SCi). Neurons in the caudal SCi, coding a target location, increase their activity during the gap, while neurons in the rostral SCi, with tonic activity related to visual fixation, decrease activity. However, the gap effect confounds two factors: (1) a goal-driven temporal warning component (upcoming saccade target appearance) and (2) a stimulus-driven sensory component (FP disappearance). These factors combine to reduce SRT and elicit pre-target responses in the SCi. To dissociate warning and sensory effects, we altered the luminance of the FP during the gap period (renamed warning period) such that it could increase, decrease, or stay the same. Faster SRTs resulted with larger decrements in FP luminance. Different categories of SCi warning period activity were evaluated: (1) always increasing or decreasing or (2) sensory-linked responses to changes in FP luminance. In the caudal SCi (at the location coding the target), all activity correlated negatively with SRT (i.e., saccade facilitation), and two categories of activity were observed (always increasing or opposing FP luminance changes). In the rostral SCi, four categories of activity were observed: activity that increased or followed the change in FP luminance correlated positively with SRT (i.e., saccade inhibition), while activity that decreased or opposed FP luminance changes correlated negatively with SRT. Such SCi activity reflected both goal-driven saccade preparation signals and FP sensory properties.
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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 | 0.000 |
| 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".