Dipper functions for second-order modulation of contrast, orientation, and motion
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Bibliographic record
Abstract
The human visual system can detect not only modulations in luminance (first-order stimuli), but also modulations of these modulations (second-order stimuli). For example modulations in the contrast, orientation, or motion of a first-order “carrier” stimulus. Second-order modulations are substantially more difficult to detect than equivalent first-order modulations. This difference would be explainable if we understood the processing mechanisms of second-order stimuli, however these are not yet clear. In order to compare the different types of second-order processing and allow comparisons to be made against first-order, we used the pedestal masking method to measure dipper functions for three types of second-order stimuli: contrast-, orientation- and motion-modulated. The contrast-modulated stimuli are constructed by modulating a 45°, 4 c/d sinusoid grating carrier with a horizontal sinusoid grating envelope with a spatial frequency of 0.5 c/d. The orientation-modulation stimuli are made by adding two contrast-modulated stimuli with perpendicular carrier gratings and opposite envelope phases together. The motion-modulation stimuli are made by adding two contrast-modulated stimuli with opposite envelope phases but the same orientation drifting in perpendicular directions. The data are fit using maximum likelihood with a modified version of the Legge & Foley (1980) contrast response function. We find the dipper shapes similar to first order (same exponents) for the contrast, orientation and motion stimuli. Compared to contrast-modulation (from which the other stimuli are constructed) both the orientation- and motion-modulation conditions show an increased saturation constant (8 and 2 times higher respectively) consistent with increased divisive suppression. The motion-modulation condition also has an increased internal noise about 1.2 times that for contrast-modulation. In comparison with first-order results from previous studies we find all three second-order conditions have increased internal noise and greatly increased saturation constants. We consider preliminary designs for model architectures that may account for our results. Meeting abstract presented at VSS 2015
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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 it