Blurred Images Lead to Bad Local Minima

High Initial visual Acuity (HIA) in newborns treated for cataracts, it is argued by Vogelsang et al. (2018), may cause impairments in configural face analysis. This HIA hypothesis is contrary to the standard explanation by a critical period for learning face processing. The hypothesis is supported by computational experiments with an artificial neural network. In our work we argue that the computational methodology used to evaluate the HIA hypothesis is flawed. It essentially shows that when a classifier is tested with images from different resolutions, the classifier benefits from seeing images from different resolution during its training. We therefore offer a better-fitting methodology; employing the modified methodology the HIA hypothesis does not hold in simulations using the same artificial neural network model, and the same data. Vogelsang et al. (2018) also show that initial exposure to low resolution images gives rise to larger receptive fields. Our last set of experiments tests the hypothesis that this might be the underlying reason for the observed impairments. Once again, we are unable to find an advantage to training with images of low initial acuity. We therefore conclude that simulations with artificial networks do not support the hypothesis that High Initial visual Acuity is detrimental.