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Front Comput Neurosci. 2014 Apr 16;8:47. doi: 10.3389/fncom.2014.00047. eCollection 2014.

An extended reinforcement learning model of basal ganglia to understand the contributions of serotonin and dopamine in risk-based decision making, reward prediction, and punishment learning.

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  • 1Department of Biotechnology, Indian Institute of Technology - Madras Chennai, India.
  • 2Department of Computer Science and Engineering, Indian Institute of Technology - Madras Chennai, India.
  • 3Foundational Processes of Behaviour Research Concentration, Marcs Institute for Brain and Behaviour & School of Social Sciences and Psychology, University of Western Sydney Sydney, NSW, Australia.


Although empirical and neural studies show that serotonin (5HT) plays many functional roles in the brain, prior computational models mostly focus on its role in behavioral inhibition. In this study, we present a model of risk based decision making in a modified Reinforcement Learning (RL)-framework. The model depicts the roles of dopamine (DA) and serotonin (5HT) in Basal Ganglia (BG). In this model, the DA signal is represented by the temporal difference error (δ), while the 5HT signal is represented by a parameter (α) that controls risk prediction error. This formulation that accommodates both 5HT and DA reconciles some of the diverse roles of 5HT particularly in connection with the BG system. We apply the model to different experimental paradigms used to study the role of 5HT: (1) Risk-sensitive decision making, where 5HT controls risk assessment, (2) Temporal reward prediction, where 5HT controls time-scale of reward prediction, and (3) Reward/Punishment sensitivity, in which the punishment prediction error depends on 5HT levels. Thus the proposed integrated RL model reconciles several existing theories of 5HT and DA in the BG.


Decision Making; Punishment; Reinforcement Learning; Reward; Risk; basal ganglia; dopamine; serotonin

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