Connecting the dots - Recognition of artificial and natural shapes relies on representing points of high information

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.