Title: Human peripheral blur is optimal for object recognition,
Abstract: Our eyes sample a disproportionately large amount of information at the
centre of gaze with increasingly sparse sampling into the periphery. This
sampling scheme is widely believed to be a wiring constraint whereby high
resolution at the centre is achieved by sacrificing spatial acuity in the
periphery. Here we propose that this sampling scheme may be optimal for object
recognition because the relevant spatial content is dense near an object and
sparse in the surrounding vicinity. We tested this hypothesis by training deep
convolutional neural networks on full-resolution and foveated images. 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. Taken
together our results suggest that, peripheral blurring in our eyes may have
evolved for optimal object recognition, rather than merely to satisfy wiring
constraints.