Dynamics of target and distractor spatial averaging in the global effect
Bibliographic record
Abstract
Background: In the global effect, saccades are displaced towards a distractor that is near in location to the target, an effect that is thought to reflect neural averaging in the superior colliculus. The temporal profile of this averaging process has not yet been investigated, however. Objective: We studied how the global effect varied with the degree of temporal dissociation between target and distractor appearance. Methods: In the first study, the target was flashed for 10ms at 8째 horizontal eccentricity, followed after an interval varying between 0ms and 100ms, by a 10ms distractor at either 4째 or 12째 horizontal eccentricity. In the second study, the distractor appeared first, either as a 100ms flash or with sustained presence, at the same locations, and followed after an interval varying between 0ms to 800ms by the target. We analyzed saccade amplitude data from 12 subjects in terms of offsets, latencies and integration time. Results: In the first experiment, the offset between the target and distractor did not influence the global effect. The global effect occurred only in saccades with latencies between 140 and 300ms, or with integration times between 80 and 360ms. In the second experiment, the global effect decreased significantly with 100ms of offset between the distractor and target, but was still evident. The global effect was stronger when the distractor was continuously present throughout the trial. Similar to the first experiment, we found the global effect only in saccades with latencies between 80 and 350ms. Conclusion: The global effect can occur despite separation of the target and distractor in time, suggesting that there is substantial persistence of distractor-related activity that is available for spatial averaging in the superior colliculus. Meeting abstract presented at VSS 2014
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".