Monocular unmasking of noise-embedded patterns.
Bibliographic record
Abstract
Binocular disparity cues may help an observer "unmask" a target in a background, thereby enhancing its detectability (e.g., Moraglia & Schneider, 1992). Here, we sought to determine whether similar effects could be produced by monocular displacement cues resulting from a two-frame sequential presentation of a Gabor pattern (a sinusoidal modulation of luminance combined with a Gaussian modulation of local contrast) embedded in an unvarying field of two-dimensional Gaussian noise. The Gabor in the second frame was spatially displaced relative to its location in the first frame; the horizontal displacement corresponded to a phase shift of the peak spatial frequency of the Gabor of 0 degree, 90 degrees, 180 degrees, 360 degrees, or 540 degrees. Monocular detection thresholds for the Gabor were appreciably lower for the 90 degrees, 180 degrees, and 540 degrees shift, than for the 0 degree and 360 degrees values. We explain these findings in terms of a model that constitutes the monocular analog of our summation model of binocular unmasking.
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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) | 0.000 | 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) | 0.002 | 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".