  In recent years, the brain and cognitive sciences have made great strides
developing a mechanistic understanding of object recognition in mature brains.
Despite this progress, fundamental questions remain about the origins and
computational foundations of object recognition. What learning algorithms
underlie object recognition in newborn brains? Since newborn animals learn
largely through unsupervised learning, we explored whether unsupervised
learning algorithms can be used to predict the view-invariant object
recognition behavior of newborn chicks. Specifically, we used feature
representations derived from unsupervised deep neural networks (DNNs) as inputs
to cognitive models of categorization. We show that features derived from
unsupervised DNNs make competitive predictions about chick behavior compared to
supervised features. More generally, we argue that linking controlled-rearing
studies to image-computable DNN models opens new experimental avenues for
studying the origins and computational basis of object recognition in newborn
animals.
