The role of contrast in shape discrimination of radial frequency patterns
Bibliographic record
Abstract
Shape processing involves a progression from a local to global analysis. A key aspect of this progression is the binding of distributed local features into an overall form, followed by the extraction of the shape independently of its local contrasts and spatial scales. Here we use radial frequency (RF) patterns in a shape discrimination task, which is thought to be based on a global processing stage of form analysis that has reached contrast and scale invariance. We compare performance across different spatial scales (contour spatial frequencies of 0.75-10.0 cpd and pattern radii of 0.5-10.0 degs) with contrast matched in multiples of stimulus detection threshold. For each possible combination of radius and spatial frequency we first measured contrast thresholds for the detection of a circular RF pattern using a 2AFC staircase procedure. We then measured shape discrimination as a function of contrast. Our results reveal an effect of spatial frequency and pattern radius on discrimination thresholds, with no scale invariance. Our results are at odds with earlier work showing no effect of scaling of radius and spatial frequency on discrimination thresholds. We also find an effect of contrast on shape discrimination for scaled contrasts below 100%. We conclude that our results do not support the assumption of a global processing stage for these stimuli. Instead, we suggest they indicate the importance of local curvature analyses. We develop a local curvature model that can predict our shape discrimination thresholds for foveal and peripheral vision.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".