Bibliographic record
Abstract
Contour interaction, the detrimental effect of flanking features on the discrimination of optotypes, has been studied mainly close to the visual acuity limit. We were interested to know how these results compare with those for the detection of targets. According to the simplest model of contour interaction, comparable detection effects would be expected. The case for low-level masking would be further strengthened if the form and nature of the dependence on flank separation and flank polarity followed that typically found in studies of lateral spatial masking [Vision Res. 33, 993 (1993)]. Landolt Cs subtending a visual angle of 0.25 degrees, 0.5 degrees, and 1.0 degrees were presented and contrast thresholds for detecting the presence of the Landolt C and discriminating its orientation were measured in five normal subjects as a function of flank separation and flank polarity. The results obtained for the relationship between detection and discrimination depend on the size of the target used. For small letters, discrimination but not detection was significantly affected by flanking bars. For large letters, detection and discrimination were affected to the same extent. However, in this case the effectiveness of opposite-polarity flanks and the finding that facilitation occurred at close, not far, flank separations suggests that the simplest explanation in terms of masking may not be applicable.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".