Local Perturbations to a Global Radial Frequency Masker Alleviate Lateral Masking Effects
Bibliographic record
Abstract
Radial Frequency (RF) contours, generated through the sinusoidal modulation of the radius of a circle, are a useful tool to study the processes involved in shape perception. Previous research examining RF contour detection suggests that low and high RF contours are processed by separate global and local shape detection mechanisms, respectively (Bell et al., 2007). If the processes responsible for global and local RF detection do not interact, then a lateral mask consisting of a combination of low and high RF contours should interfere with the detection of a low RF contour at least as much as a low RF contour mask alone. To test this prediction, we measured detection thresholds for a low RF contour (RF5) in the presence of a control mask (RF0), a low RF mask (RF5), a high RF mask (RF25), or a compound mask (RF5+RF25) consisting of the combination of RF5 and RF25 patterns. Consistent with previous reports, two out of the three observers show significant masking with the low RF mask relative to the control and high RF mask. Critically, these two observers showed significantly less masking for the compound mask than for the low RF mask, and did not show a significant difference in masking with the compound mask relative to the control and high RF mask. The third, anomalous, observer showed relatively high levels of masking across all conditions, including the control mask. Overall, however, our results suggest that global and local shape detection mechanisms do not operate independently of one another in masking. We currently are examining the extent to which the results reveal individual differences, and how the nature of RF interactions influences masking. Meeting abstract presented at VSS 2014
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".