Before revision:  We tested this hypothesisby training deep convolutional neural networks on full-resolution and foveatedimages. Our main finding is that networks trained on images with foveated sampling show better object classification compared to networks trained on full resolution images. Importantly, blurring images according to the human blur function yielded the best performance compared to images with shallower or steeper blurring.
After revision:  We tested this hypothesisby training deep convolutional neural networks on 'foveated' images with high resolution near objects and increasingly sparse sampling into the periphery. Neural networks trained using a blur profile matching the human eye yielded the best performance compared to images with shallower or steeper blurring.