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 …

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

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 …

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 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 …

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 …

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 …

A number of accounts of human and animal behavior posit the operation of parallel and
competing valuation systems in the control of choice behavior. In these accounts, a flexible but …

Recent computational theories of decision making in humans and animals have portrayed 2
systems locked in a battle for control of behavior. One system—variously termed model-free …

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, …