Understanding how humans and other animals are able to learn from experience and use this information to select future behavioural strategies to obtain the reinforcers necessary for survival, is a fundamental research question in biology. Considerable progress has been made in recent years on the neural computational underpinnings of this process following the observation that the phasic activity of dopamine neurons in the midbrain resembles a prediction error from a formal computational theory known as reinforcement learning (RL). While much is known about the functions of dopamine in RL, much less is known about the computational functions of other modulatory neurotransmitter systems in the midbrain such as the cholinergic, norcpinephrine, and serotonergic systems. The goal of this research proposal to the ERC, is to begin a systematic study of the computational functions of these other neurotransmitter systems (beyond dopamine) in RL. To do this we will combine functional magnetic resonance imaging in human subjects while they perform simple decision making tasks and undergo pharmacological manipulations to modulate systemic levels of these different neurotransmitter systems. We will combine computational model-based analyses with fMRI and behavioural data in order to explore the effects that these pharmacological modulations exert on different parameters and modules within RL. Specifically, we will test the contributions that the cholinergic system makes in setting the learning rate during RL and in mediating computations of expected uncertainty in the distribution of rewards available, we will test for the role of norepinephrine in balancing the rate of exploration and exploitation during decision making, as well as in encoding the level of unexpected uncertainty, and we will explore the possible role of serotonin in setting the rate of temporal discounting for reward, or in encoding prediction errors during aversive as opposed to reward-learning.
Field of science
- /natural sciences/computer and information sciences/artificial intelligence/machine learning/reinforcement learning
- /engineering and technology/medical engineering/diagnostic imaging/magnetic resonance imaging
Call for proposal
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