Figure 5.

Principal component analyses. a, When projected into two dimensions, the similarity of the correlation matrices of the different ROIs can be visualized. The first component, which already explains 81.8% variance, shows a clear separation between ROIs with and without effects of viewpoint symmetry (the second component explains an additional 13.9% of the variance). b, The resulting first component when computing a PCA directly on the entries of the correlation matrices. The component exhibits large weights in the two diagonals, in direct agreement with the effects of low-level similarity, and viewpoint symmetry.