Units and features of the deep neural network architecture were similar as proposed in (Krizhevsky et al., 2012). All deep neural networks were identical with the exception of the number of nodes in the last layer (output layer) as dictated by the number of training categories, i.e. 683 for the deep object network, 216 for deep scene network. Abbreviations: Conv = Convolutional layer, Pool = Pooling layer; Norm = Normalization layer; FC1-3 = fully connected layers. The 8 layers referred to in the manuscript correspond to the convolution stage for layers 1–5, and the FC103 stage for layers 6–8 respectively.