The Neural Correlates of Ambiguity and Risk in Human Decision-Making under an Active Inference Framework

Abstract

Active inference integrates perception, decision-making, and learning into a united theoretical framework, providing an efficient way to trade off exploration and utilization by minimizing (expected) free energy. In this study, we asked how the brain represents values, uncertainty, and resolves the uncertainty under the active inference framework in the exploration-exploitation trade-off. 25 participants performed a contextual two-step two-armed bandit task, with electroencephalogram (EEG) recordings. By comparing the fitting results from the active inference and reinforcement learning model, we show that active inference can better capture the exploration instinct of humans, which helps resolve the uncertainty of the environment. The EEG sensor-level results show that the activity in the frontal, central, and parietal regions is associated with uncertainty, while activity in the frontal and central brain regions is associated with risk. The EEG source-level results indicate that the expected free energy is encoded in the lateral occipital cortex and the uncertainty in the middle temporal pole. Our study dissociates the expected free energy and the uncertainty in active inference theory and their neural correlates, suggesting the reliability of active inference in characterizing cognitive processes of human decisions. It provides behavioral and neural evidence of active inference in decision processes and insights into the neural mechanism of human decision under different kinds of uncertainty.