RT Journal Article
SR Electronic
T1 Reinforcement meta-learning optimizes visuomotor learning
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.01.19.912048
DO 10.1101/2020.01.19.912048
A1 Taisei Sugiyama
A1 Nicolas Schweighofer
A1 Jun Izawa
YR 2020
UL http://biorxiv.org/content/early/2020/01/20/2020.01.19.912048.abstract
AB Reinforcement learning enables the brain to learn optimal action selection, such as go or not go, by forming state-action and action-outcome associations. Does this mechanism also optimize the brain’s willingness to learn, such as learn or not learn? Learning to learn by rewards, i.e., reinforcement meta-learning, is a crucial mechanism for machines to develop flexibility in learning, which is also considered in the brain without empirical examinations. Here, we show that humans learn to learn or not learn to maximize rewards in visuomotor learning tasks. We also show that this regulation of learning is not a motivational bias but is a result of an instrumental, active process, which takes into account the learning-outcome structure. Our results thus demonstrate the existence of reinforcement meta-learning in the human brain. Because motor learning is a process of minimizing sensory errors, our findings uncover an essential mechanism of interaction between reward and error.