The mask-onset delay paradigm and the availability of central and peripheral visual information during scene viewing
Bibliographic record
Abstract
We employed a variant of the mask-onset delay paradigm in order to limit the availability of visual information in central and peripheral vision within individual fixations during scene viewing. Subjects viewed full-color scene photos with instructions to search for a target object (Experiment 1) or to study them for a later memory test (Experiment 2). After a fixed interval following the onset of each eye fixation (50-100 ms), the scene was scrambled either in the central visual field or over the entire display. The intact scene was presented when the subject made an eye movement. Our results reconcile different sets of findings from prior research regarding the masking of central and peripheral visual information at different intervals following fixation onset. In particular, we found that when the entire display was scrambled, both search and memory performance were impaired even at relatively long mask-onset intervals. In contrast, when central vision was scrambled, there were subtle impairments that depended on the viewing task. In the 50-ms mask-onset interval, subjects were selectively impaired at identifying, but not in locating, the search target (Experiment 1), while memory performance (Experiment 2) was unaffected in this condition, and hence, the reliance on central and peripheral visual information depends partly on the viewing task.
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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) | 0.001 | 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 | 0.001 |
| 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".