Neurocomputational mechanism of controllability inference under a multi-agent setting.

ABSTRACT: 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-out

SUBMITTER: Kim J 

PROVIDER: S-EPMC8604335 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature