Beyond core object recognition: Recurrent processes account for object recognition under occlusion
Fig 3
Generalization across time and occlusion levels.
(a) The classifier is trained on an occlusion level (e.g. 0% occlusion) and tested on the other occlusion level (e.g. 60% occlusion). Time-points with significant decoding accuracy are shown inside the dashed contours (right-sided signrank test, FDR-corrected across time, p<0.05). The contour of significant time-points has a shift towards the upper side of the diagonal when the classifier is trained with 0% occlusion and tested on 60% occlusion (i.e. 63% of significant time points are above the diagonal) whereas in the lower right matrix we see the opposite pattern (66% of significant time points are located below the diagonal). (b) The two color maps below the decoding matrices show the difference between the two decoding matrices located above them.