After growing up together, and mostly growing apart in the second half of the 20th century,
the fields of artificial intelligence (AI), cognitive science, and neuroscience are reconverging …

A key problem in statistical modeling is model selection, that is, how to choose a model at an
appropriate level of complexity. This problem appears in many settings, most prominently in …

Reinforcement learning is the process by which an agent learns to predict long-term future
reward. We now understand a great deal about the brain's reinforcement learning algorithms, …

Recent progress in artificial intelligence has renewed interest in building systems that learn
and think like people. Many advances have come from using deep neural networks trained …

The mesostriatal dopamine system is prominently implicated in model-free reinforcement
learning, with fMRI BOLD signals in ventral striatum notably covarying with model-free …

A cognitive map has long been the dominant metaphor for hippocampal function, embracing
the idea that place cells encode a geometric representation of space. However, evidence …

We review the psychology and neuroscience of reinforcement learning (RL), which has
experienced significant progress in the past two decades, enabled by the comprehensive …

… We thank Nathaniel Daw, Michael Todd, Anatole Gershman, and Ken Norman for helpful
discussions.Correspondence concerning this article should be addressed to Samuel J. …

Theories of reward learning in neuroscience have focused on two families of algorithms
thought to capture deliberative versus habitual choice. ‘Model-based’ algorithms compute the …

In recent years, ideas from the computational field of reinforcement learning have revolutionized
the study of learning in the brain, famously providing new, precise theories of how …