Book ChapterDOI
Understanding the role of serotonin in basal ganglia through a unified model
Balasubramani Pragathi Priyadharsini,Balaraman Ravindran,V. Srinivasa Chakravarthy +2 more
- pp 467-473
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A Reinforcement Learning (RL)-based model of serotonin which tries to reconcile some of the diverse roles of the neuromodulator is presented, which uses a novel formulation of utility function, which is a weighted sum of the traditional value function and the risk function.Abstract:
We present a Reinforcement Learning (RL)-based model of serotonin which tries to reconcile some of the diverse roles of the neuromodulator. The proposed model uses a novel formulation of utility function, which is a weighted sum of the traditional value function and the risk function. Serotonin is represented by the weightage, α, used in this combination. The model is applied to three different experimental paradigms: 1) bee foraging behavior, which involves decision making based on risk, 2) temporal reward prediction task, in which serotonin (α) controls the time-scale of reward prediction, and 3) reward/punishment prediction task, in which punishment prediction error depends on serotonin levels. The three diverse roles of serotonin --- in time-scale of reward prediction, risk modeling, and punishment prediction --- is explained within a single framework by the model.read more
Citations
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Posted ContentDOI
A model of opposing counsels in human decision-making
TL;DR: A temporal difference model of human decision-making is introduced to account for positive and negative errors, and it is demonstrated that the model can learn about reward expectations and uncertainty, and provide information about reaction time despite not modeling these variables directly.
Book ChapterDOI
Modeling Precision Grip Force in Controls and Parkinson’s Disease Patients
TL;DR: A Go/Explore/NoGo (GEN) algorithm in a utility-based decision-making framework is presented to explain the SM generated by healthy controls and PD patients both during ON and OFF medication.
Journal ArticleDOI
Modeling task-specific manifestations of serotonin in basal ganglia using risk-based decision making
TL;DR: This model effectively reconciles not only the diverse functions of 5HT but also predicts that BG computes utility rather than value, a feature that differentiates from several valuebased actor-critic models of BG.
References
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Book
Reinforcement Learning: An Introduction
TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
Book ChapterDOI
Prospect theory: an analysis of decision under risk
Daniel Kahneman,Amos Tversky +1 more
TL;DR: In this paper, the authors present a critique of expected utility theory as a descriptive model of decision making under risk, and develop an alternative model, called prospect theory, in which value is assigned to gains and losses rather than to final assets and in which probabilities are replaced by decision weights.
Journal ArticleDOI
Prospect theory: analysis of decision under risk
Daniel Kahneman,Amos Tversky +1 more
Journal ArticleDOI
A Neural Substrate of Prediction and Reward
TL;DR: Findings in this work indicate that dopaminergic neurons in the primate whose fluctuating output apparently signals changes or errors in the predictions of future salient and rewarding events can be understood through quantitative theories of adaptive optimizing control.
Book
Introduction to Reinforcement Learning
TL;DR: In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning.