Computational model of dopamine and serotonin in decision making

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  • Tanaka Saori
    Institute of Social and Economic Research, Osaka University

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  • 予測と意思決定におけるドーパミン，セロトニンの計算論的モデル
  • ヨソク ト イシ ケッテイ ニ オケル ドーパミン,セロトニン ノ ケイサンロンテキ モデル

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Abstract

To elucidate complex brain function, a computational approach is widely accepted in system neuroscience as well as clinical disciplines. In particular, computational models of neuromodulators, such as dopamine and serotonin, are indispensable to make clear the neural system of prediction and decision- making, and these models have been frequently tested using an experimental approach. Prediction error signal in reinforcement learning theory is a representative computational model for the role of dopamine in prediction and decision- making. This model was proposed on the basis of electrophysiological data from a series of studies on dopaminergic neurons in monkeys conducted by Schultz et al. in the 1990s. In classical conditioning experiment, dopaminergic neurons in monkeys responded to rewards before learning, whereas after the learning tasks, these neurons started to respond to the conditioned stimuli. This neuronal alteration observed in dopaminergic neurons was revealed to be similar to the prediction error signal in reinforcement learning. Based on this new discovery of the role of dopaminergic neurons, reinforcement learning model mediated by the cortico- basal ganglia circuit has been proposed, and this model is supported by studies using electrophysiological techniques and functional magnetic resonance imaging of human brain. The representative role of serotonin in prediction and decision- making may be “impulsive choice” behavior. Impulsive choice is defined as a behavioral preference of immediate small rewards over distant large rewards, and rats developed impulsive choice behavior when the serotonergic system in the brain was destroyed. On the basis of these findings, it has been proposed that serotonin is involved in delay discounting. However, because serotonergic neurons have a wide range of projection and a large number of serotonin receptor subtypes exist, many questions remain about the functional role of serotonin in impulsive choice. Consequently, various computational models of serotonin, including temporal discounting, have been proposed and are being investigated.

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