Computational models of reinforcement learning: the role of dopamine as a reward signal

By R. Samson, Michael J Frank, Jean-Marc Fellous. 2010

70 citations recorded by the source corpus.

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

1. Doina Precup
2. Irina Rish
3. Kent C. Berridge
4. Khimya Khetarpal
5. Matthew Riemer
6. Joseph E LeDoux
7. Peter S. Dayan
8. Morten L. Kringelbach

In Cognitive Neuroscience

1. Doina Precup
2. Irina Rish
3. Kent C. Berridge
4. Khimya Khetarpal
5. Matthew Riemer
6. Joseph E LeDoux
7. Jaak Panksepp
8. Morten L. Kringelbach

In Molecular Biology

1. Doina Precup
2. Irina Rish
3. Kent C. Berridge
4. Khimya Khetarpal
5. Matthew Riemer
6. Morten L. Kringelbach
7. Suzanne Hidi

Citations in the corpus

• Towards Continual Reinforcement Learning: A Review and Perspectives (2020, background): “Indeed, there has been a significant body of research trying to understand the role of the dopamine system as a reward signal for humans (Samson et al., 2010; Starkweather et al., 2017).”
• From prediction error to incentive salience: mesolimbic computation of reward motivation (2012, background): “Also, dopamine manipulation by drugs have been reported to change learning and learned performance in people (Pessiglione et al., 2006; Moustafa et al., 2008; Samson et al., 2010).”