Reinforcement learning (RL) algorithms provide powerful explanations for simple learning
and decision-making behaviors and the functions of their underlying neural substrates …

'Learning to learn'phenomena have been widely investigated in cognition, perception and
more recently also in action. During concept learning tasks, for example, it has been …

Over the past 20 years, neuroscience research on reward-based learning has converged on
a canonical model, under which the neurotransmitter dopamine 'stamps in'associations …

The dilemma between information gathering (exploration) and reward seeking (exploitation)
is a fundamental problem for reinforcement learning agents. How humans resolve this …

Learning and executive functions such as task-switching share common neural substrates,
notably prefrontal cortex and basal ganglia. Understanding how they interact requires …

How do children and adults differ in their search for rewards? We considered three different
hypotheses that attribute developmental differences to (a) children's increased random …

Patch foraging presents a sequential decision-making problem widely studied across
organisms—stay with a current option or leave it in search of a better alternative? Behavioral …

Despite many studies on selective attention, fundamental questions remain about its nature
and neural mechanisms. Here I draw from the animal and machine learning fields that …

An accessible introduction to constructing and interpreting Bayesian models of perceptual
decision-making and action. Many forms of perception and action can be mathematically …

Recent findings have demonstrated that reward feedback alone can drive motor learning.
However, it is not yet clear whether reward feedback alone can lead to learning when a …