Reward expectations based on internal knowledge of the external environment are a core
component of adaptive behavior. However, internal knowledge may be inaccurate or …

Dopamine is implicated in adaptive behavior through reward prediction error (RPE) signals
that update value estimates. There is also accumulating evidence that animals in structured …

Decisions occur in dynamic environments. In the framework of reinforcement learning, the
probability of performing an action is influenced by decision variables. Discrepancies …

In computer science, reinforcement learning is a powerful framework with which artificial
agents can learn to maximize their performance for any given Markov decision process …

The ability to make optimal decisions depends on evaluating the expected rewards
associated with different potential actions. This process is critically dependent on the fidelity …

Highlights•Decision-making involves various uses of internal models, such as reflecting
reward structures.•Generalized prediction errors are used to improve value-based decision …

Predicting outcomes is a critical ability of humans and animals. The dopamine reward
prediction error hypothesis, the driving force behind the recent progress in neural “value …

When learning the value of actions in volatile environments, humans often make seemingly
irrational decisions that fail to maximize expected value. We reasoned that these 'non …

Animals can adapt their preferences for different types for reward according to physiological
state, such as hunger or thirst. To describe this ability, we propose a simple extension of …

Learning to predict future outcomes is critical for driving appropriate behaviors.
Reinforcement learning (RL) models have successfully accounted for such learning, relying …