There is and has been a fruitful flow of concepts and ideas between studies of learning in
biological and artificial systems. Much early work that led to the development of …

We review the current state of knowledge on the computational and neural mechanisms of
reinforcement-learning with a particular focus on fronto-striatal circuits. We divide the …

Reinforcement learning allows organisms to predict future outcomes and to update their
beliefs about value in the world. The dorsal-lateral prefrontal cortex (dlPFC) integrates …

The explore-exploit dilemma refers to the challenge of deciding when to forego immediate
rewards and explore new opportunities that could lead to greater rewards in the future …

A frequent assumption in value-based decision-making tasks is that agents make decisions
based on the feature dimension that reward probabilities vary on. However, in complex …

Reinforcement learning is a theoretical framework that describes how agents learn to select
options that maximize rewards and minimize punishments over time. We often make …

Despite being unpredictable and uncertain, reward environments often exhibit certain
regularities, and animals navigating these environments try to detect and utilize such …

Decisions in the natural world are rarely made in isolation. Each action that an organism
selects will affect the future situations in which it finds itself, and those situations will in turn …

Biological agents adapt behavior to support the survival needs of the individual and the
species. In this review we outline the anatomical, physiological, and computational …

The striatum plays critical roles in visually-guided decision-making and receives dense
axonal projections from midbrain dopamine neurons. However, the roles of striatal …