Recent work has paired classic category learning models with convolutional neural networks (CNNs), allowing researchers to study categorization behavior from raw image inputs. However, this research typically uses naturalistic images, which assess participant responses to existing categories; yet, much of traditional category learning research has focused on using novel, artificial stimuli to examine the learning process behind how people acquire categories. In this work, we pair a CNN with ALCOVE (Kruschke, 1992), a well-known exemplar model of categorization, and attempt to examine whether this model can reproduce the classic type ordering effect from Shepard, Hovland, and Jenkins (1961) on raw images rather than abstract features. We examine this question with a variety of CNN architectures and image datasets and compare ALCOVE-CNN to two other models that lacked certain key features of ALCOVE. We found that our ALCOVE-CNN model could reproduce the type ordering effect more often than the other models we tested, but in limited situations. Our results showed that success varied greatly across the various configurations we tested, suggesting that the feature representations from CNNs provide strong constraints in properly capturing this effect.

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 (DNNs) show a shape or texture bias by constructing images with conflicting shape and texture cues. They found that DNNs strongly preferred to classify familiar objects based on texture as opposed to shape. 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 range of neural networks. We find that DNNs exhibit a preference for shape rather than texture when tested under conditions that more closely replicate the developmental procedure.