The representational hierarchy in human and artificial visual systems in the presence of object-scene regularities
Fig 6
Multiple domain-specific object spaces.
The RSA results for the 4 models (GIST, condition, animacy continuum, navigational layout) are shown for group-averaged brain (left) data and DCNNs (right). For the neural data, only ROIs where the domain model reached significance were included (see Fig 2A). The same DCNN architecture (GoogLeNet) was trained either on object recognition (ImageNet), or scene recognition (Scene 365). For comparison with the first RSA analysis (see Fig 3A), gray shaded areas indicate the network’s layers in which the domain model significantly outperformed the remaining models. Color-coded lines on top of bar/graphs indicate the network’s layers/ROIs where each model significantly outperformed the remaining models (p < 0.001) computed with pairwise permutations tests (10000 randomizations of stimulus labels).