Saccade execution suppresses discrimination at distractor locations rather than enhancing the saccade goal location
Bibliographic record
Abstract
As we have limited processing abilities with respect to the plethora of visual information entering our brain, spatial selection mechanisms are crucial. These mechanisms result in both enhancing processing at a location of interest and in suppressing processing at other locations; together, they enable successful further processing of locations of interest. It has been suggested that saccade planning modulates these spatial selection mechanisms; however, the precise influence of saccades on the distribution of spatial resources underlying selection remains unclear. To this end, we compared discrimination performance at different locations (six) within a work space during different saccade tasks. We used visual discrimination performance as a behavioral measure of enhancement and suppression at the different locations. A total of 14 participants performed a dual discrimination/saccade countermanding task, which allowed us to specifically isolate the consequences of saccade execution. When a saccade was executed, discrimination performance at the cued location was never better than when fixation was maintained, suggesting that saccade execution did not enhance processing at a location more than knowing the likelihood of its appearance. However, discrimination was consistently lower at distractor (uncued) locations in all cases where a saccade was executed compared with when fixation was maintained. Based on these results, we suggest that saccade execution specifically suppresses distractor locations, whereas attention shifts (with or without an accompanying saccade) are involved in enhancing perceptual processing at the goal location.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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".