Multiple model-based reinforcement learning explains dopamine neuronal activity.

Abstract

A number of computational models have explained the behavior of dopamine neurons in terms of temporal difference learning. However, earlier models cannot account for recent results of conditioning experiments; specifically, the behavior of dopamine neurons in case of variation of the interval between a cue stimulus and a reward has not been satisfyingly accounted for. We address this problem by using a modular architecture, in which each module consists of a reward predictor and a value estimator. A "responsibility signal", computed from the accuracy of the predictions of the reward predictors, is used to weight the contributions and learning of the value estimators. This multiple-model architecture gives an accurate account of the behavior of dopamine neurons in two specific experiments: when the reward is delivered earlier than expected, and when the stimulus-reward interval varies uniformly over a fixed range.