Proceedings of the Annual Meeting of the Cognitive Science Society

Humans can flexibly bind familiar functional roles to novel entities in their environment. For example, children who have the concept of ``goal posts'' can bind this abstract role to two hats placed on the street. In doing so, they can port over existing expectations of ``goal posts" for the duration of the game. In this paper, we seek to explore artificial agents' ability to perform flexible role binding and rebinding. To this end, we designed a Gridworld navigation game and tested a popular CNN-based agent which has had success in other tasks involving visual and spatial state spaces (e.g. Atari or Minigrid). To our surprise, we found that while this architecture was capable of overfitting to the training set, it was not able to learn flexible role binding without intervention. We ultimately show that with carefully engineered data augmentation techniques, our artificial agent is able to learn the task. This suggests that the diversity of the training dataset was a limiting factor.