Building compositional tasks with shared neural subspaces.

Cognition is remarkably flexible; we are able to rapidly learn and perform many different tasks. Theoretical modeling has shown artificial neural networks trained to perform multiple tasks will re-use representations and computational components across tasks. By composing tasks from these sub-components, an agent can flexibly switch between tasks and rapidly learn new tasks.