Computational and neural mechanisms of affected beliefs

Abstract

The feedback people receive on their behavior shapes the process of belief formation and self-efficacy in mastering a given task. However, the neural and computational mechanisms of how the subjective value of these beliefs, and the corresponding affect, bias the learning process remain unclear. We investigated these mechanisms during the learning of self-efficacy beliefs using fMRI, pupillometry, and computational modeling, and by analyzing individual differences in affective experience. Biases in the formation of self-efficacy beliefs were associated with affect, pupil dilation, and neural activity within the anterior insula, amygdala, ventral tegmental area/ substantia nigra, and mPFC. Specifically, neural and pupil responses map the valence of the prediction errors in correspondence with individuals experienced affect and learning bias during belief formation. Together with the functional connectivity dynamics of the anterior insula within this network, our results hint at neural and computational mechanisms that integrate affect into the process of belief formation.