  Han et al. (2020) reported a behavioral experiment that assessed the extent
to which the human visual system can identify novel images at unseen retinal
locations (what the authors call "intrinsic translation invariance") and
developed a novel convolutional neural network model (an Eccentricity Dependent
Network or ENN) to capture key aspects of the behavioral results. Here we show
that their analysis of behavioral data used inappropriate baseline conditions,
leading them to underestimate intrinsic translation invariance. When the data
are correctly interpreted they show near complete translation tolerance
extending to 14{\deg} in some conditions, consistent with earlier work (Bowers
et al., 2016) and more recent work Blything et al. (in press). We describe a
simpler model that provides a better account of translation invariance.
