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 …

The outcome of a decision is often uncertain, and outcomes can vary over repeated
decisions. Whether decision outcomes should substantially affect behaviour and learning …

The meta-reinforcement learning (meta-RL) framework, which involves RL over multiple
timescales, has been successful in training deep RL models that generalize to new …

Striatal dopamine drives associative learning by acting as a teaching signal. Much work has
focused on simple learning paradigms, including Pavlovian and instrumental learning …

Ensuring safety and achieving human-level driving performance remain challenges for
autonomous vehicles, especially in safety-critical situations. As a key component of artificial …

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 …

The neural mechanisms that support naturalistic learning via effective pedagogical
approaches remain elusive. Here we used functional near-infrared spectroscopy to measure …

Previous research has stressed the importance of uncertainty for controlling the speed of
learning, and how such control depends on the learner inferring the noise properties of the …

The estimated values of choices, and therefore decision-making based on those values, are
influenced by both the chance that the chosen items or goods can be obtained (availability) …

Reinforcement learning (RL) is the behavioral process of learning the values of actions and
objects. Most models of RL assume that the dopaminergic prediction error signal drives …