Neural basis of reinforcement learning and decision making.
By Daeyeol Lee, Hyojung Seo, M. Jung. 2012
500 citations recorded by the source corpus.
Reinforcement learning is an adaptive process in which an animal utilizes its previous experience to improve the outcomes of future choices. Computational theories of reinforcement learning play a central role in the newly emerging areas of neuroeconomics and decision neuroscience. In this framework, actions are chosen according to their value functions, which describe how much future reward is expected from each action. Value functions can be adjusted not only through reward and penalty, but also by the animal’s knowledge of its current environment. Studies have revealed that a large proportion of the brain is involved in representing and updating value functions and using them to choose an action. However, how the nature of a behavioral task affects the neural mechanisms of reinforcement learning remains incompletely understood. Future studies should uncover the principles by which different computational elements of reinforcement learning are dynamically coordinated across the entire brain.
Subfields: Cognitive Neuroscience, General Decision Sciences, Reinforcement Learning
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In Cognitive Neuroscience
In Reinforcement Learning
Citations in the corpus
- Survey of Hallucination in Natural Language Generation (2022, background): “The purpose of RL is for the agent to learn an optimal or nearly-optimal policy that maximizes the reward that accumulates from the environment [91, 178].”
- An Introduction to Deep Reinforcement Learning (2018, unknown): “…about the connections between RL and neuroscience and, as such, the reader is referred to the work of Sutton and Barto (2017), Niv (2009), Lee et al. (2012), Holroyd and Coles (2002), Dayan and Niv (2008), Dayan and Daw (2008), Montague (2013), and Niv and Montague (2009) for an in-depth…”
- The minority report: some common assumptions to reconsider in the modelling of the brain and behaviour (2016, background): “This calls for an investigation of ‘the principles by which different computational elements of reinforcement learning are dynamically coordinated across the entire brain’ (Lee, Seo, & Jung, 2012).”