Computational psychiatry is a rapidly growing field attempting to translate advances in
computational neuroscience and machine learning into improved outcomes for patients …

Computer scientists have long recognized that naive implementations of algorithms often
result in a paralyzing degree of redundant computation. More sophisticated implementations …

Humans routinely formulate plans in domains so complex that even the most powerful
computers are taxed. To do so, they seem to avail themselves of many strategies and …

We propose that humans spontaneously organize environments into clusters of states that
support hierarchical planning, enabling them to tackle challenging problems by breaking …

Psychiatric disorders profoundly impair many aspects of decision making. Poor choices
have negative consequences in the moment and make it very hard to navigate complex …

Humans can learn many novel tasks from a very small number (1–5) of demonstrations, in
stark contrast to the data requirements of nearly tabula rasa deep learning methods. We …

Humans use prior knowledge to efficiently solve novel tasks, but how they structure past
knowledge during learning to enable such fast generalization is not well understood. We …

Compositionality is a central component of the human faculty for generalization and
flexibility. However, the computations involved are poorly understood, especially in terms of …

Flexibility is one of the hallmarks of human problem‐solving. In everyday life, people adapt
to changes in common tasks with little to no additional training. Much of the existing work on …

Understanding the inductive biases that allow humans to learn in complex environments has
been an important goal of cognitive science. Yet, while we have discovered much about …