Attentional modulation of saccadic inhibition during scene viewing
Bibliographic record
Abstract
Participants viewed eight-item arrays containing colour photographs from two categories of scenes. Four of the eight photos depicted natural landscapes (Nature scenes) and the other four depicted urban environments (Building scenes). Participants were instructed to memorize scenes from one of the two categories (i.e., the relevant category) in preparation for a later recognition memory test. A gaze-contingent manipulation was employed such that while a scene was being fixated, the border around it flickered briefly from black to white with a random interval between flickers ranging from 400 – 600 ms. We computed the likelihood of a saccade being initiated in the period following the flicker. Consistent with prior research, we observed a saccadic inhibition effect with a minimum in saccadic activity occurring roughly 97 ms following the flicker. Importantly, the saccadic inhibition effect was stronger in magnitude and duration when the eye was fixated on a relevant scene compared to an irrelevant scene. This finding corroborates and extends prior research on the relationship between saccadic inhibition and attention in reading, and demonstrates that the saccadic inhibition effect can provide an index of the deployment of attention during scene viewing. Implications of these findings for theories of attention and oculomotor control are discussed. Meeting abstract presented at VSS 2012
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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 | 0.003 |
| 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".