Comparing Pupil Light Response Modulation between Saccade Planning and Working Memory
Bibliographic record
Abstract
The signature of spatial attention effects has been demonstrated through saccade planning and working memory. Although saccade planning and working memory have been commonly linked to attention, the comparison of effects resulting from saccade planning and working memory is less explored. It has recently been shown that spatial attention interacts with local luminance at the attended location. When bright and dark patch stimuli are presented simultaneously in the periphery, thereby producing no change in global luminance, pupil size is nonetheless smaller when the locus of attention overlaps with the bright, compared to the dark patch stimulus (referred to as the local luminance modulation). Here, we used the local luminance modulation to directly compare the effects of saccade planning and spatial working memory. Participants were required to make a saccade towards a visual target location (visual-delay) or a remembered target location (memory-delay) after a variable delay, and the bright and dark patch stimuli were presented during the delay period between target onset and go signal. Greater pupil constriction was observed when the bright patch, compared to the dark patch, was spatially aligned with the target location in both tasks. However, the effects were diminished when there was no contingency implemented between the patch and target locations, particularly in the memory-delay task. Together, our results suggest the involvement of similar, but not identical, attentional mechanisms through saccade planning and working memory, and highlight a promising potential of local pupil luminance responses for probing visuospatial processing.
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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 | 0.001 |
| 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".