Sven Collette, Wolfgang M Pauli ... John O'Doherty
The human brain is capable of implementing inverse reinforcement learning, where an observer infers the hidden reward structure of a decision problem solely through observing another individual take actions.
Gail M Rosenbaum, Hannah L Grassie, Catherine A Hartley
Relative to children and adults, adolescents placed greater weight on negative prediction errors during learning and these age-varying learning idiosyncrasies biased subsequent memory for information associated with valenced outcomes.
fMRI evidence for off-task replay predicts subsequent replanning behavior in humans, suggesting that learning from simulated experience during replay helps update past policies in reinforcement learning.
Yumeya Yamamori, Oliver J Robinson, Jonathan P Roiser
A computational and translational approach to measuring anxiety-related behaviour was validated in two large independent samples, presenting opportunities for cross-species anxiety research and potential implications for anxiolytic drug development.
John P Grogan, Demitra Tsivos ... Elizabeth J Coulthard
Memory over 24 hours was impaired in Parkinson's patients off, rather than on, dopaminergic medication during reinforcement learning, whereas dopamine did not affect positive and negative reinforcement, in contrast to previous studies.
Collaborative hunting, characterized by the division of roles among predators, has emerged within a group of artificial agents through deep reinforcement learning.
Longitudinal and computational analyses reveal an early and temporally stable hippocampal and striatal involvement in reinforcement learning in 6-to-7-year-old children.
Dimensions for reinforcement learning reduced by dynamic organization of cerebellar climbing fiber response and synchrony in multiple functional components.
Challenging a popular theory in neuroeconomics, a computational cognitive study provides evidence against divisive normalization, a supposedly canonical neural computation, in favor of an alternative account, range normalization, in the context of value learning.
Computational modeling suggests that feedback between striatal cholinergic neurons and spiny neurons dynamically adjusts learning rates to optimize behavior in a variable world.
