Evaluating Phase Dependent Masking with Radial Frequency Contours
Bibliographic record
Abstract
Previous work on spatial contextual interactions between shapes defined by radial frequency (RF) contours has demonstrated that such interactions depend on the rotational phase alignment between a mask and target stimulus (Habak et al., 2004; 2009). When the points of maximum curvature in target and masking RF contours are aligned (i.e., zero phase difference), thresholds for detecting deviations from circularity are significantly elevated relative to a baseline condition that contains no masking stimulus, and the strength of masking declines as the relative phase difference increases (Habak et al., 2004; 2009). The current study extended this previous work by examining the effect of RF number on the magnitude of threshold elevation observed across seven target-mask relative phases. We measured detection thresholds for five RF contours (RF3, 5, 6, 8, 11) in the presence of a surrounding mask of the same RF number as the target in seven target-mask relative phase combinations (0°, 30°, 60°, 90°, 120°, 150°, 180°). Although the amount of masking varied considerably across RF contours, we found that the magnitude of masking declined with increasing relative target-mask phase difference for all RF combinations. However, we also found a significant interaction between RF number and phase such that the rate at which masking declined with phase differed across RF contours. Overall, these results suggest significant differences exist in how the frequency of local curvature affects the interference observed as a function of the alignment between two shapes. The results of this study serve as a foundation for interpreting the effect of rotational phase alignment between spatially separated shapes. Meeting abstract presented at VSS 2015
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".