Model-based predictions for dopamine
By Angela J. Langdon, M. Sharpe, Geoffrey Schoenbaum, Yael Niv. 2017
134 citations recorded by the source corpus.
Phasic dopamine responses are thought to encode a prediction-error signal consistent with model-free reinforcement learning theories. However, a number of recent findings highlight the influence of model-based computations on dopamine responses, and suggest that dopamine prediction errors reflect more dimensions of an expected outcome than scalar reward value. Here, we review a selection of these recent results and discuss the implications and complications of model-based predictions for computational theories of dopamine and learning.
Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Molecular Biology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cellular and Molecular Neuroscience
In Cognitive Neuroscience
- Milena Rmus
- Doina Precup
- Irina Rish
- Khimya Khetarpal
- Matthew Riemer
- A. G. Collins
- Samuel David McDougle
In Molecular Biology
Citations in the corpus
- Towards Continual Reinforcement Learning: A Review and Perspectives (2020, background): “Li et al. (2006) provide a unified view on the theory of state abstractions.”
- The Neuroscience of Drug Reward and Addiction. (2019, unknown): “…from what is expected, DA signals a “reward prediction error” regardless of its positive or negative valence, that recent studies suggest may reflect not just its scalar reward value but additional dimensions of the expected outcome, such as its characteristics or presentation sequence (189).”
- The Role of Executive Function in Shaping Reinforcement Learning. (2020, background): “secondary conditioning) and more abstract learning of associations based on sensory features [56, 57].”