A “Gap Effect” on Stop Signal Reaction Times in a Human Saccadic Countermanding Task
Bibliographic record
Abstract
The "gap effect" describes a phenomenon whereby saccadic reaction times are expedited by the removal of a visible fixation point prior to target presentation. Here we investigated whether processes controlling saccade cancellation are also subjected to a gap effect. Human subjects performed a countermanding experiment that required them to try to cancel an impending saccade in the presence of an imperative visual stop signal, across different fixation conditions. We found that saccadic cancellation latencies, estimated via derivation of the stop signal reaction time (SSRT), were approximately 40 ms shorter on trials with a 200-ms gap between fixation point removal and target presentation compared with when the fixation point remained illuminated. Follow-up experiments confirmed that the reduction in SSRTs were primarily due to removal of a foveal fixation point (as opposed to a generalized warning effect) and persisted with an auditory stop signal that controlled for potential differences in stop signal saliency across different fixation conditions. Saccadic RTs exhibited a gap effect in all experiments with reductions in RTs being due to both removal of a foveal fixation point and a generalized warning effect. Overall, our results demonstrate that processes controlling saccade cancellation can be expedited by a 200-ms gap. The simultaneous priming of both saccade cancellation and generation is of particular interest considering the mutually antagonistic relationship between the saccade fixation and generation networks in the oculomotor system.
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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 | 0.004 |
| 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".