The effect of distractors in prosaccade, antisaccade, and memory-guided saccade tasks
Bibliographic record
Abstract
Saccade trajectories are modulated by the presence of an irrelevant distractor. These modifications have been attributed to competitive interactions of activation patterns in the superior colliculus, a midbrain structure involved in encoding stimuli as potential saccade targets. Variations in the balance between target and distractor activations in different saccadic paradigms may therefore be reflected in differential effects of distractors on the saccadic trajectory. To explore this, we investigated the effect of distractors in prosaccade, antisaccade, and memory-guided saccade tasks. We hypothesized that target activation would be reduced in antisaccades (where there was never a stimulus at the target location) and in memory-guided saccades (where the stimulus at target location has disappeared). If so, a model of competition between target and distractor activations would predict that distractors would induce greater distortions of saccadic trajectory in antisaccades and memory-guided saccades, than in prosaccades. In the same set of 8 subjects we measured vertical prosaccades, antisaccades and memory-guided saccades (using a 1500 ms interval) with and without a distractor. The results showed that deviation away from the irrelevant distractor was larger in an antisaccade than in a prosaccade. Still more deviation away was observed in the memory-guided saccade paradigm. These results confirm the hypothesis that a distractor evokes more competition when there is a weaker target representation. They also illustrate the potential of using saccade deviations as a probe of saccade-related activation patterns in the ocular motor 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".