Perceptual enhancement as a result of a top-down attentional influence in a scene viewing task: Evidence from saccadic inhibition
Bibliographic record
Abstract
Prior research has shown that task instructions influence the locations and durations of eye fixations during scene viewing. These task-related changes in gaze patterns are likely to be associated with a top-down influence of attention. Presently we applied a saccadic-inhibition manipulation in order to detect another expected manifestation of top-down attention: perceptual enhancement. Participants viewed eight-item arrays containing photographs from two categories of scenes. Four of the photos depicted natural landscapes ("nature") and the other four depicted urban environments ("buildings"). Participants were instructed to memorize scenes from one of the categories in preparation for a later recognition memory test. During eye fixations the border around the fixated scene flickered briefly from black to white with a random interval between flickers ranging from 400 to 600 ms. We computed the likelihood of a saccade being initiated in the period following the flicker. Consistent with prior research, we found a strong saccadic inhibition effect with maximum saccadic inhibition occurring approximately 97 ms following the flicker. Importantly, the saccadic inhibition effect was stronger and longer lasting when the subject's eyes were fixated on a relevant scene compared to an irrelevant scene. These findings are consistent with perceptual enhancement as a result of top-down attention.
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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) | 0.001 | 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".