Figure 4.

Example and counterexample stimuli for the trend towards lower identifiability for stimuli with very large numbers of interior elements, which according to the final model was stronger for smaller numbers of contour elements. Identification rates for outlines are taken from Wagemans et al (2008). Identification rates shown for Gabor arrays are averaged across all Gabor versions (RCR version depicted here). Large numbers of interior elements combined with small numbers of contour elements correspond to compact shapes which can lack diagnosticity even as full outlines, leading to matching problems. The top-left panel shows one case (number 102 ‘garbage can’) where these problems were augmented by the relatively coarse Gabor rendering rather than (exclusively) being carried over from the full outline. The relatively dull and smooth features of compact shapes can nevertheless be strongly diagnostic in some cases (eg number 204 ‘shoe’, bottom left). Larger numbers of contour elements tend to correspond to stimuli with more pronounced, but large and smooth, features, enabling successful grouping and matching (number 33 ‘bow’, bottom right). When outline curvature is too strong, grouping can fail, or can provide an inadequate representation of the underlying shape (number 87 ‘fence’, top right).