Spatial and temporal dynamics of presaccadic attentional facilitation before pro- and antisaccades
Bibliographic record
Abstract
The premotor theory of attention and the visual attention model make different predictions about the temporal and spatial allocation of presaccadic attentional facilitation. The current experiment investigated the spatial and temporal dynamics of presaccadic attentional facilitation during pro- and antisaccade planning; we investigated whether attention shifts only to the saccade goal location or to the target location or elsewhere, and when. Participants performed a dual-task paradigm with blocks of either anti- or prosaccades and also discriminated symbols appearing at different locations before saccade onset (measure of attentional allocation). In prosaccades blocks, correct prosaccade discrimination was best at the target location, while during errors, discrimination was best at the location opposite to the target location. This pattern was inversed in antisaccades blocks, although discrimination remained high opposite to the target location. In addition, we took the benefit of a large range of saccadic landing positions and showed that performance across both types of saccades was best at the actual saccade goal location (where the eye will actually land) rather than at the instructed position. Finally, temporal analyses showed that discrimination remained highest at the saccade goal location, from long before to closer to saccade onset, increasing slightly for antisaccades closer to saccade onset. These findings are in line with the premises of the premotor theory of attention, showing that attentional allocation is primarily linked both temporally and spatially to the saccade goal location.
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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 | 0.002 |
| 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) | 0.002 | 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".