Abstract
The belief that we can exert an influence in our environment is dependent on distinct components of perceived control. Here, we investigate the neural representations that differentially code for self-efficacy (belief in successfully executing a behavior) and response-efficacy (belief that the behavior leads to an expected outcome) and how such signals may be integrated to inform decision-making. Participants provided confidence ratings related to executing a behavior (self-efficacy), and the potential for a rewarding outcome (response-efficacy). Computational modeling was used to measure the subjective weight of self-efficacy and response-efficacy while making decisions and to examine the neural mechanisms of perceived control computation. While participants factored in both self-efficacy and response-efficacy during decision-making, we observed that integration of these two components was dependent on neural responses within the vmPFC, OFC and striatum. Further, the dlPFC was observed to assign importance to self-efficacy and response-efficacy in specific trials, while dACC computed the trade-off between both components, taking into account individual differences. These findings highlight the contributions of perceived control components in decision-making, and identify key neural pathways involved in computing perceived control.