A unified framework and normative dataset for second-order sensitivity using the quick Contrast Sensitivity Function (qCSF)
Bibliographic record
Abstract
Introduction: While the contrast sensitivity approach has been successful in quantifying striate function, there is a need to develop comparable ways of evaluating extra-striate function in humans. Neurophysiologically, the extra-striate cortex differs from the striate cortex in a number of important ways. Second-order modulated stimuli are thought to be processed by the visual system in two serial stages; the carrier is processed by the localized, spatially bandpass neurons in V1 and in a second stage the rectified V1 output is integrated in extra-striate cortex. Here, our purpose is to establish normative data on the sensitivity of extra-striate human cortical function. Methods: We optimally designed second-order stimuli contrast-, orientation- or motion-modulated in order to reflect extra-striate function. We use a common novel methodology, the quick contrast sensitivity function (qCSF) method, recently developed for the rapid measurement of visual contrast sensitivity across a range of spatial frequencies relevant to striate function (Lesmes et al., 2010). This method is a Bayesian adaptive procedure that estimates multiple parameters of the sensitivity function and concurrently estimates thresholds across the full spatial-frequency range. It was originally built to determine the first-order contrast sensitivity function, but here we use it for determining both first and second-order functions. Results: We first show that the qCSF methodology can be well adapted to different kinds of first- and second-order measurements. We provide a normative dataset (102 eyes) for first- and second-order sensitivity and we show that the sensitivity to all these stimuli is equal in the two eyes. Conclusions: Our results confirm some strong differences between first- and second-order processing, in accordance with the classical filter-rectify-filter model. They suggest a unique contrast detection mechanism but different second-order ones. 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 | 0.012 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".