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 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 | 0.025 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".