Bibliographic record
Abstract
Knock-out is a new form of visual masking, distinguished by robustness to temporal onset and considerable indifference to the visual appearance of the mask (Cramer & Rensink, VSS, 2012). Indeed, some localized masks are as effective as their larger-scale counterparts. We investigate here the extent to which knock-out is sensitive to grouping. As before, observers detected a change in an alternating display containing an array of line segments, one of which changed orientation by 45° on half the trials. The duration of each display was 60 ms and the inter-stimulus interval (ISI) was 420 ms. When a mask was present, it appeared at the location of each item for 100 ms during the ISI, with a stimulus onset asynchrony (SOA) of 220 ms. Twelve observers were tested for each type of mask. Masks varied along overall spatial extent, and ease with which their constituents could form separate groups. Results showed that knock-out depended on spatial extent: masks that were bounded (i.e., terminated before they reached the edge of the screen) had a greater effect than those that were unbounded (i.e., appeared to extend off-screen). Grouping was also important: if the elements of an extended mask could form a group that was was bounded, substantial impairment resulted. Interestingly, this impairment did not rely on the elements being in one-to-one correspondence with the targets: performance suffered even when mask elements did not overlap any targets. These results suggest that the masking responsible for knock-out involves at least two processes: one that acts locally, and one that involves larger-scale structures that are largely irrelevant if not part of the same "framework" or "region" of the scene. They also suggest that knock-out itself is a useful way to explore the nature of these larger-scale structures, and the kinds of grouping processes involved. Meeting abstract presented at VSS 2013
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".