Detection of global shape in radial frequency patterns involves interacting contour shape channels operating independently of local form processes
Bibliographic record
Abstract
Radial frequency (RF) patterns have been used to examine the processes involved in shape perception. Current literature suggests that there are distinct global and local shape detection processes for low and high radial frequency patterns, but this has not been tested in a combined contour pattern, such as would be needed to represent the contours of most natural objects. To test this, we combined RF components onto a single path to make a compound RF structure. Psychophysical thresholds for detecting a single RF component were measured and compared to thresholds for detecting the same component in a compound pattern. Experiment 1 combined frequencies from the local and global range. The results show that local and global RF shape information can be detected independently despite appearing on the same contour; suggesting separate processes that can function in parallel. For the range of RF patterns that are processed globally, multiple curvature mechanisms are proposed to account for human performance. To test for interactions, Experiment 2 combined RF components within the global range. Masking was present, and was tuned for RF, suggesting the existence of several narrow band shape channels. Adaptation was then used to selectively desensitise channels. Adapting to a single RF pattern reduced sensitivity to RF patterns of similar frequency but improved sensitivity to a dissimilar RF component on the compound contour. Effects were shown to be independent of the radius of the adaptor, and also occurred with adaptors composed of contrast modulated noise, suggesting that post-adaptation results are not contaminated by fatiguing of local V1 orientation-tuned cells. The results are consistent with at least two contour shape channels, which operate in a competitive network. The findings are discussed in relation to current models of shape perception.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".