  Visual translation tolerance refers to our capacity to recognize objects over
a wide range of different retinal locations. Although translation is perhaps
the simplest spatial transform that the visual system needs to cope with, the
extent to which the human visual system can identify objects at previously
unseen locations is unclear, with some studies reporting near complete
invariance over 10{\deg} and other reporting zero invariance at 4{\deg} of
visual angle. Similarly, there is confusion regarding the extent of translation
tolerance in computational models of vision, as well as the degree of match
between human and model performance. Here we report a series of eye-tracking
studies (total N=70) demonstrating that novel objects trained at one retinal
location can be recognized at high accuracy rates following translations up to
18{\deg}. We also show that standard deep convolutional networks (DCNNs)
support our findings when pretrained to classify another set of stimuli across
a range of locations, or when a Global Average Pooling (GAP) layer is added to
produce larger receptive fields. Our findings provide a strong constraint for
theories of human vision and help explain inconsistent findings previously
reported with CNNs.
