  Early in development, children learn to extend novel category labels to
objects with the same shape, a phenomenon known as the shape bias. Inspired by
these findings, Geirhos et al. (2019) examined whether deep neural networks
show a shape or texture bias by constructing images with conflicting shape and
texture cues. They found that convolutional neural networks strongly preferred
to classify familiar objects based on texture as opposed to shape, suggesting a
texture bias. However, there are a number of differences between how the
networks were tested in this study versus how children are typically tested. In
this work, we re-examine the inductive biases of neural networks by adapting
the stimuli and procedure from Geirhos et al. (2019) to more closely follow the
developmental paradigm and test on a wide range of pre-trained neural networks.
Across three experiments, we find that deep neural networks exhibit a
preference for shape rather than texture when tested under conditions that more
closely replicate the developmental procedure.
