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

A case for robust translation tolerance in humans and CNNs. A commentary on Han et al