Dipper functions for second-order modulation of contrast, orientation, and motion
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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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".