Computational and neural mechanisms of affected beliefs

TLDR: In this paper, the authors investigated the neural and computational mechanisms of how the subjective value of these beliefs and corresponding affect bias the learning process of self-efficacy beliefs using fMRI, pupillometry, computational modeling and individual differences in affective experience.

Abstract: The feedback people receive on their behavior shapes the process of belief formation and self-efficacy in mastering a given task. The neural and computational mechanisms of how the subjective value of these beliefs and corresponding affect bias the learning process are yet unclear. Here we investigate this question during learning of self-efficacy beliefs using fMRI, pupillometry, computational modeling and 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, VTA/SN, and mPFC. Specifically, neural and pupil responses map the valence of the prediction errors in correspondence to the experienced affect and learning bias people show during belief formation. Together with the functional connectivity dynamics of the anterior insula within this network our results hint towards neural and computational mechanisms that integrate affect in the process of belief formation.