A neural model of symmetry perception for curved shapes
Bibliographic record
Abstract
Introduction. Using global shape cues, humans discriminate circles from radial frequency (RF) patterns at hyperacuity levels (Wilkinson, Wilson & Habak, VR1998). Here, we extend our neural model of RF perception (Poirier & Wilson, VSS2005) to account for human perception of symmetry in biologically-relevant shapes (Wilson & Wilkinson, VR2002). Model. Object position is estimated using large-scale non-Fourier V4-like concentric units, which encode the center of concentric contour segments across orientations. Further processing occurs relative to the estimated object center, providing translation invariance. Shape information is retrieved using curvature mechanisms' responses to visual contours. Curvature mechanisms are scaled with distance from object center, providing scale invariance. Curvature responses were highest at points of maximum curvature, encoding their number, amplitudes, and locations. Symmetry was defined as the correlation of neural curvature responses on either side of a symmetry axis, and the symmetry axis' orientation was defined as the orientation at which symmetry peaked. Results. Symmetry perception for faces and complex shapes depends on whether curvature extrema positions are symmetrical (Wilson & Wilkinson, VR2002), which depends on the phase-alignment of the component RFs used to create the shape. In our model, symmetry decreased faster with phase-misalignment for stimuli associated with low thresholds in psychophysical experiments. Discussion. This represents the first model of symmetry shape perception for biologically-relevant shapes (e.g. faces) defined as complex RF patterns. Our model is compatible with recent data on V4 and IT population coding (e.g. Brincat & Connor, NatNeuro2004; Pasupathy & Connor, NatNeuro2002).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".