ERC Starting Grant · 2020
Reinforcement learning (RL) characterizes how we adaptively learn, by trial and errors, to select actions that maximize the occurrence of rewards, and minimize the occurrence of punishments. While the behavioural, computational and neurobiological features of learning from singular experienced outcomes have been extensively studied, the mechanisms by which RL could leverage multiple, concurrent information samples – including abstract information about prospective outcomes – have been largely overlooked. As a consequence, little is known about how we prioritize, filter or value outcome information in RL, while these processes likely critically contribute to shaping learning behaviour. This…
From the public funding record at EU CORDIS. Describes the funded project, not the reviews below.