I am interested in cognitive flexibility and generalization, and how these abilities are enabled by factors like language, memory, and embodiment. I am also interested in when and how intelligence fails. I consider these issues from both the perspective of human cognition and artificial intelligence.

My work often draws inspiration from cognitive science to build better AI; for example improving RL agent memory using the idea of mental time-travel, using task relationships to adapt to novel tasks on the first try, and learning from explanations. I also use methods and paradigms from cognitive science to study the behavior of AI systems; for example exploring whether large language models can learn from explanations in context, are affected by content on logical reasoning tasks, and can process center embedded syntactic structures. Finally, I consider broader issues such as how we should think about symbols in AI and how publishing fast and slow could improve the generalizability of research.