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 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".