Understanding dopamine and reinforcement learning: the dopamine reward prediction error hypothesis.

@article{Glimcher2011UnderstandingDA,
  title={Understanding dopamine and reinforcement learning: the dopamine reward prediction error hypothesis.},
  author={Paul Glimcher},
  journal={Proceedings of the National Academy of Sciences of the United States of America},
  year={2011},
  volume={108 Suppl 3},
  pages={15647-54}
}
A number of recent advances have been achieved in the study of midbrain dopaminergic neurons. Understanding these advances and how they relate to one another requires a deep understanding of the computational models that serve as an explanatory framework and guide ongoing experimental inquiry. This intertwining of theory and experiment now suggests very clearly that the phasic activity of the midbrain dopamine neurons provides a global mechanism for synaptic modification. These synaptic… CONTINUE READING
Highly Influential
This paper has highly influenced 22 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Related Discussions
This paper has been referenced on Twitter 6 times. VIEW TWEETS

Citations

Publications citing this paper.
Showing 1-10 of 220 extracted citations

Microstimulation of the human substantia nigra alters reinforcement learning.

The Journal of neuroscience : the official journal of the Society for Neuroscience • 2014
View 6 Excerpts
Highly Influenced

Socially dependent avoidance learning

View 5 Excerpts
Highly Influenced

Hierarchical learning induces two simultaneous, but separable, prediction errors in human basal ganglia.

The Journal of neuroscience : the official journal of the Society for Neuroscience • 2013
View 5 Excerpts
Highly Influenced

The effects of the previous outcome on probabilistic choice in rats.

Journal of experimental psychology. Animal behavior processes • 2013
View 12 Excerpts
Highly Influenced

References

Publications referenced by this paper.
Showing 1-10 of 67 references

Reinforcement learning: the good, the bad and the ugly.

Current opinion in neurobiology • 2008
View 8 Excerpts
Highly Influenced

Reinforcement Learning: An Introduction

IEEE Transactions on Neural Networks • 1998
View 10 Excerpts
Highly Influenced

The neuroeconomic theory of learning

A Caplin, M Dean
Am Econ Rev • 2007
View 5 Excerpts
Highly Influenced

A framework for mesencephalic dopamine systems based on predictive Hebbian learning.

The Journal of neuroscience : the official journal of the Society for Neuroscience • 1996
View 5 Excerpts
Highly Influenced

A Theory of the Striatum (Pergamon, Oxford), 1st Ed

JR Wickens
1993
View 4 Excerpts
Highly Influenced

A mathematical model for simple learning.

Psychological review • 1951
View 10 Excerpts
Highly Influenced

Similar Papers

Loading similar papers…