Proceedings of the Annual Meeting of the Cognitive Science Society

People use rich prior knowledge about the world in order toefficiently learn new concepts. These priors–also known as“inductive biases”–pertain to the space of internal models con-sidered by a learner, and they help the learner make inferencesthat go beyond the observed data. A recent study found thatdeep neural networks optimized for object recognition developthe shape bias (Ritter et al., 2017), an inductive bias possessedby children that plays an important role in early word learning.However, these networks use unrealistically large quantities oftraining data, and the conditions required for these biases to de-velop are not well understood. Moreover, it is unclear how thelearning dynamics of these networks relate to developmentalprocesses in childhood. We investigate the development andinfluence of the shape bias in neural networks using controlleddatasets of abstract patterns and synthetic images, allowing usto systematically vary the quantity and form of the experienceprovided to the learning algorithms. We find that simple neuralnetworks develop a shape bias after seeing as few as 3 exam-ples of 4 object categories. The development of these biasespredicts the onset of vocabulary acceleration in our networks,consistent with the developmental process in children.