The temporal dynamics of target and distractor occurrence in the global effect of saccades
Bibliographic record
Abstract
Background: In the global effect, saccades are displaced in the direction of a distractor near to the target, an effect that may reflect weighted averaging of neural activity in a collicular map. However, the dynamics of the temporal relationship between distractor and target necessary to generate the global effect are not known. Objective: Our goal was to determine the impact of temporal dissociations between the distractor and target on the global effect. Methods: In the first experiment, 12 subjects performed saccades to targets at 8° eccentricity on the horizontal meridian, with and without distractors located at either 4° (near) or 12° (far) eccentricity. Both targets and distractors appeared for 10 ms only. Distractors appeared at 0 ms (simultaneous), 20, 30, 40, 50 or 60 ms after target onset. In the second experiment, 12 other subjects performed a similar experiment with a wider range of temporal offsets, of 0, 20, 40, 60, 70, 80, 90 and 100 ms. The global effect was reflected in the difference in saccade amplitude between conditions with near versus far distractors. These amplitude data were analyzed first as a function of target-distractor offset. Next we assessed them as a function of integration time, meaning the time between distractor onset and saccade onset. Results: Both experiments showed that robust global effect could still be obtained with large target-distractor asynchronies, even up to 100 ms. The integration time analysis showed that a global effect could be generated by integration times as low as 90 ms, and maximal for integration times of 100–160 ms. Conclusions: Simultaneous onset of targets and distractors are not essential for the global effect. Distractors can generate a global effect even if they appear only 90 ms before the saccade is made, which is shorter than the 130–140 ms ‘constant reaction time’ found in studies using double-step saccade paradigms.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".