Comparing human MEG and behavioral data with feedforward and recurrent computational models of visual hierarchy.

(a) Time-varying representational similarity analysis between human MEG data and the computational models. We, first, obtained representational dissimilarity matrices (RDM) for each computational model—using feature values of the layer before the softmax operation—, and for the MEG data at each time-point. For each subject, their MEG RDMs were correlated (Spearman’ R) with the computational model RDMs (i.e. AlexNet & HRRN) across time; the results were then averaged across subjects. (b, c) Time-courses of RDM correlations between the models and the human MEG data. HRRN readout stage 0 represents the purely feedforward version of HRRN. Thicker lines show significant time points (right-sided signrank test, FDR-corrected across time, p (d) Object recognition performance of humans (mask and no-mask trials) and models [AlexNet and HRRN-ReadoutStage-0 (feedforward) and HRRN(recurrent)] across different levels of occlusion. We evaluated model accuracies on a multiclass recognition task similar to the multiclass behavioral experiment done in humans (S6 Fig). The models’ performances were calculated by holding out an occlusion level for testing, and training a SVM classifier on the remaining levels of occlusion. Error bars are SEM.