Controllability perception significantly influences motivated behavior and emotion and requires an estimation of one’s influence on an environment. Previous studies have shown that an agent can infer controllability by observing contingency between one’s own action and outcome if there are no other outcome-relevant agents in an environment. However, if there are multiple agents who can influence the outcome, estimation of one’s genuine controllability requires exclusion of other agents’ possible influence. Here, we first investigated a computational and neural mechanism of controllability inference in a multi-agent setting. Our novel multi-agent Bayesian controllability inference model showed that other people’s action-outcome contingency information is integrated with one’s own action-outcome contingency to infer controllability, which can be explained as a Bayesian inference. Model-based functional MRI analyses showed that multi-agent Bayesian controllability inference recruits the temporoparietal junction (TPJ) and striatum. Then, this inferred controllability information was leveraged to increase motivated behavior in the vmPFC. These results generalize the previously known role of the striatum and vmPFC in single-agent controllability to multi-agent controllability, and this generalized role requires the TPJ in addition to the striatum of single-agent controllability to integrate both self- and other-related information. Finally, we identified an innate positive bias toward the self during the multi-agent controllability inference, which facilitated behavioral adaptation under volatile controllability. Furthermore, low positive bias and high negative bias were associated with increased daily feelings of guilt. Our results provide a mechanism of how our sense of controllability fluctuates due to other people in our lives, which might be related to social learned helplessness and depression.

Despite having more direct information with functional magnetic resonance spectroscopy (fMRS), little is known about how the brain’s neurochemical mechanisms influence mental health and decision-making. In this study, we examined whether baseline glutamate/glutamine (Glx) and lactate concentrations were related to general anxiety and depression and how they changed according to gains and losses with fMRS scanning during reward learning and computational modeling. The Glx concentration in the anterior insular cortex (AIC), not the medial prefrontal cortex, was positively associated with the general psychopathological factor of anxiety and depression through the mediation of error sensitivity. The AIC Glx level decreased mainly after learning from gains and as the entire task was performed. Conversely, lactate reduction in AIC occurred only during learning from losses and was associated with higher general psychopathology scores. We demonstrated for the first time that both glutamate-related error sensitivity and loss-specific lactate reduction in the AIC are associated with general psychopathology. This study’s results suggest that the AIC is a potential target brain region for glutamate and/or lactate-mediated therapeutics for anxiety and depression.