An image-computable model of human visual shape similarity
Fig 6
ShapeComp predicts perceptual distortions in human shape similarity across shape arrays.
Four example shape sets (A, B, C, D) sampled uniformly in GAN space (top row). To test whether subtle perceptual distortions in humans were systemically deviated away from GAN space towards ShapeComp, these shape sets were selected such that the pairwise distances of shapes in ShapeComp varied slightly from GAN (with Pearson correlation values between 0.5 < r < 0.75). The arrays are distorted by ShapeComp (second row) in similar ways to humans (third row; mean across 16 participants). Across arrangements, shapes with same colour are also the same. (E) Non-uniformities for individual participants (dots) in 4 shape sets (A-D, colours). Squares show average across subjects for given set, where error bars show ± 2 standard errors. ShapeComp accounted for perceptual distortions away from the original GAN coordinates better than GAN+noise model. (F) Correlation of ShapeComp distortion with human distortion as a function of the diversity of shapes across the shape set (measured as cumulated variance in shape set across ShapeComp dimensions). Human distortions better line up with ShapeComp when there is more diversity across shape sets as predicted by ShapeComp. Grey reference line shows y = x.