Investigating the shape of the contrast sensitivity function using white, bandpass, and contrast jitter noise
Bibliographic record
Abstract
An equivalent noise experiment was conducted to investigate the effect of spatial frequency on contrast sensitivity. Under the linear amplifier model, performance can be accounted for by the efficiency of the mechanism responsible for detecting the target (relative to an ideal observer) and the variance of its internal noise. Previous studies have found conflicting results as to whether efficiency varies with spatial frequency, or if the threshold differences are due entirely to changes in internal noise variance. These experiments have frequently used broadband noise, which has the disadvantage of also activating non-target mechanisms. This leads to additional threshold elevation due to cross-channel masking (through the contrast gain pool), resulting in a confound in experiments where the relationship between the noise and target spectra is not constant. Baker & Meese [2012, Journal of Vision, 12(10):20, 1-12] proposed a novel noise masking method, where the noise is simply a contrast-jittered version of the target. This injects the noise directly into the target mechanism, minimising contrast gain pool effects. In this study, observers detected a horizontal log-Gabor target at five spatial frequencies (0.25 – 4 c/deg) in three types of noise: broadband (2D white), tuned to the target channel (2D noise filtered to have the same power spectrum as the target), and tuned to the target mechanism (contrast jitter). For each noise type, the fitted internal noise variance parameter increased with spatial frequency. In 2D white noise the fitted efficiency parameter increased with spatial frequency from 17% to 55%. In 2D filtered noise and contrast jitter noise efficiency was flat across spatial frequency at 59% and 88% respectively. By tuning our noise to the target mechanism at each spatial frequency we show that efficiency is constant, and that the decline of the contrast sensitivity function arises solely from increasing internal noise. Meeting abstract presented at VSS 2014
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".