Connecting the dots - Recognition of artificial and natural shapes relies on representing points of high information
Bibliographic record
Abstract
Physiological and psychophysical evidence suggests that the visual system represents object outlines using prominent curvature features, particularly regions of extreme curvature (convex maxima and concave minima). These curvature extrema often coincide with points of high informational content ("surprisal"), but this relationship is only correlational. It remains unclear whether the visual system explicitly encodes curvature extrema or instead prioritizes the most informative contour locations. To address this, we conducted two shape-matching experiments comparing the roles of curvature extrema and surprisal in shape representation. Observers performed match-to-sample tasks in which smooth reference shapes were matched to simplified polygonal versions created by connecting subsets of contour points corresponding to (i) curvature maxima, (ii) curvature maxima and minima, or (iii) points of highest surprisal. Stimuli included artificial shapes composed of compound radial frequency patterns and natural shapes (animal outlines), the latter allowing us to dissociate curvature and information by restricting sampled points. Performance was higher for natural than artificial shapes (95% vs. ∼86%). Shapes defined by a small number of high-surprisal points matched performance in baseline and curvature maxima and minima conditions, but exceeded performance for curvature maxima alone (∼90% vs. ∼65%). In a second experiment, we contrasted surprisal with curvature maxima and minima conditions while varying the number of sampled points (4-32). Performance increased with point number, approaching ∼90%. Critically, under strong simplification (4-6 points), surprisal-based shapes yielded higher accuracy than curvature-based shapes. These findings suggest that shape representation emphasizes features with high informational content rather than curvature extrema per se.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".