Investigating cognitive load modulation of distractor processing using pupillary luminance responses in the anti‐saccade paradigm
Bibliographic record
Abstract
Observers must select goal-directed stimuli in lieu of distractors in the environment for preferential information processing. This selection, according to the load theory of attention, is modulated by cognitive load, involving the frontal cortices, with more significant distractor interference under high cognitive load, with strained executive control resources. Evidence in support of this theory exists; however, working memory tasks were predominately used in these investigations. The influence of other types of cognitive load on distractor processing is largely unknown. An interleaved pro- and anti-saccade task has often been used to investigate executive control in which subjects are instructed in advance to either automatically look at the peripheral stimulus (pro-saccade), or to suppress the automatic response and voluntarily look in the direction opposite of the stimulus (anti-saccade). Distinct frontal preparatory activity has been clearly characterized during preparation for pro- and anti-saccades, with higher inhibition-related activity in preparation for anti-saccades than pro-saccades. Here, we used an interleaved pro- and anti-saccade paradigm to investigate the modulation of distractor interference by cognitive load in a group of 24 healthy young adults. Luminant distractors were used to evoke automatic pupillary responses to evaluate distractor processing. Greater pupillary dilation following dark distractor presentation was observed in the anti-saccade than the pro-saccade preparation. These effects, however, were absent in pupillary constriction following bright distractors. Together, our results support the load theory of attention, importantly highlighting the potential of using involuntary changes in pupil size to objectively investigate attentional selection under load.
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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 | 0.000 |
| 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".