Neurocomputational mechanism of controllability inference under a multi-agent setting

Author summaryHow we perceive controllability over an outcome if there are multiple other agents who can simultaneously influence that outcome? Previous 'single-agent' studies showed that an agents' inferred controllability depends on contingency between its own action and following outcome and this inference involves striatum. Here, we show that in a multi-agent setting, other people's action-outcome contingency information is integrated with one's own action-outcome contingency to infer controllability, which was explained as a biased Bayesian inference. Notably, bias in inference played an adaptive role under volatile controllability and was associated with a perception of guilt. Striatum and temporoparietal junction (TPJ) were involved in this multi-agent Bayesian controllability inference and this controllability information was leveraged to increase motivated behavior in the vmPFC. Our results first provide a neurocomputational mechanism of multi-agent controllability inference. 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.

Automated summary

This study investigates the neural computational mechanisms of controllability inference in a multi-agent setting. It reveals that information on others' action-outcome contingencies is integrated with one's own action-outcome contingencies to infer controllability, impacting motivated behavior. Additionally, a positive bias towards the self in multi-agent controllability inference is identified, affecting behavioral adaptation under volatile controllability.