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 …

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 …

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 …

Adaptive decision making in dynamic environments requires multiple reinforcement-learning
steps that may be implemented by dissociable neural circuits. Here, we used a novel …

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 …

Adverse childhood experiences (ACEs) are extreme stressors that lead to negative
psychosocial outcomes in adulthood. Nonhuman animals explore less after exposure to …

Tool-path codes output by computer-aided manufacturing software for high-speed
machining are composed of discontinuous G01 line segments. The discontinuity of these …

Reinforcement learning (RL), particularly in primates, is often driven by symbolic outcomes.
However, it is usually studied with primary reinforcers. To examine the neural mechanisms …

Goal-directed behavior requires identifying objects in the environment that can satisfy
internal needs and executing actions to obtain those objects. The current study examines …

Anterior cingulate cortex (ACC) and striatum (STR) contain neurons encoding not only the
expected values of actions, but also the value of stimulus features irrespective of actions …