A normative framework for the study of second-order sensitivity in vision
Bibliographic record
Abstract
While the contrast sensitivity approach has been successful in evaluating the processing of first-order stimuli, there is a need to develop comparable ways of assessing second-order vision. Our purpose here is to establish normative data on second-order contrast-, orientation-, and motion-modulation sensitivity in humans. We propose a unified framework, applying the quick contrast sensitivity function (qCSF) method, which was recently developed for the rapid measurement of contrast sensitivity across the full spatial-frequency range (Lesmes, Lu, Baek, & Albright, 2010), to measure both first- and second-order sensitivity functions. We first show that the qCSF methodology can be successfully adapted to different kinds of first- and second-order measurements. We provide a normative dataset for both first- and second-order sensitivity, and we show that the sensitivity to all these stimuli is equal in the two eyes. Our results confirm some strong differences between first- and second-order processing, in accordance with the classical filter-rectify-filter model. They suggest a common contrast detection mechanism but different second-order mechanisms.
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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 | 0.002 | 0.001 |
| 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".