Bibliographic record
Abstract
Introduction. Curvature encoding serves as an intermediate step towards the neural representation of shapes and object parts (Loffler, Wilson & Wilkinson, 2003, Vision Research; Wilkinson, Wilson & Habak, 1998, Vision Research). We compared the performance of several curvature-encoding schemes (inspired by computational, physiological, and psychophysical considerations) with respect to natural constraints imposed by shape perception tasks. Methods. Using an image filtering approach, we evaluate the response properties of different curvature encoding schemes, with respect to (1) object size variability for size constancy, (2) curvature amplitude, (3) response noise away from regions of stimulus curvature, and (4) 1st order to 2nd order contour alignment for application to texture edges or 2nd order contours. Results. Results indicate that: (1) to combine successive edge elements, an “AND” operator is preferable to a linear sum filter to reduce neural noise away from loci of maximum curvature, (2) filter properties need to be adjusted to object size otherwise systematic distortions in the locations of response maxima may occur, (3) introducing orientation-selectivity to the inputs to the curvature mechanism only modestly sharpens curvature responses in well-defined isolated contours, and (4) opponent-curvature mechanisms have greater spatial- and curvature-selectivity. Discussion. By developing an understanding of success and failures of different mechanisms, we isolated the requirements of neural curvature mechanisms. We provide important constraints on the design of biologically plausible curvature filters for use in object processing models. We also discuss a fast method of implementing position-dependent curvature mechanisms that can be used to recover curvature responses independent of object size.
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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 | 0.003 |
| 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) | 0.004 | 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".