Evidence for a link between the experiential allocation of saccade preparation and visuospatial attention
Bibliographic record
Abstract
Whether a link exists between the two orienting processes of saccade preparation and visuospatial attention has typically been studied by using either sensory cues or predetermined rules that instruct subjects where to allocate these limited resources. In the real world, explicit instructions are not always available and presumably expectations shaped by previous experience play an important role in the allocation of these processes. Here we examined whether manipulating two experiential factors that clearly influence saccade preparation--the probability and timing of saccadic responses--also influences the allocation of visuospatial attention. Occasionally, a visual probe was presented whose spatial location and time of presentation varied relative to those of the saccade target. The proportion of erroneous saccades directed toward this probe indexed saccade preparation, and the proportion of correct discriminations of probe orientation indexed visuospatial attention. Overall, preparation and attention were significantly correlated to each other across these manipulations of saccade probability and timing. Saccade probability influenced both preparation and attention processes, whereas saccade timing influenced only preparation processes. Unexpectedly, discrimination ability was not improved in those trials in which the probe triggered an erroneous saccade despite particularly heightened levels of saccade preparation. To account for our results, we propose a conceptual dual-purpose threshold model based on neurophysiological considerations that link the processes of saccade preparation and visuospatial attention. The threshold acts both as the minimum activity level required for eliciting saccades and a maximum level for which neural activity can provide attentional benefits.
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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 | 0.001 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".