  When agents interact socially with different intentions, conflicts are
difficult to avoid. Although how agents can resolve such problems autonomously
has not been determined, dynamic characteristics of agency may shed light on
underlying mechanisms. The current study focused on the sense of agency (SoA),
a specific aspect of agency referring to congruence between the agent's
intention in acting and the outcome. Employing predictive coding and active
inference as theoretical frameworks of perception and action generation, we
hypothesize that regulation of complexity in the evidence lower bound of an
agent's model should affect the strength of the agent's SoA and should have a
critical impact on social interactions. We built a computational model of
imitative interaction between a robot and a human via visuo-proprioceptive
sensation with a variational Bayes recurrent neural network, and simulated the
model in the form of pseudo-imitative interaction using recorded human body
movement data. A key feature of the model is that each modality's complexity
can be regulated differently with a hyperparameter assigned to each module. We
first searched for an optimal setting that endows the model with appropriate
coordination of multimodal sensation. This revealed that the vision module's
complexity should be more tightly regulated than that of the proprioception
module. Using the optimally trained model, we examined how changing the
tightness of complexity regulation after training affects the strength of the
SoA during interactions. The results showed that with looser regulation, an
agent tends to act more egocentrically, without adapting to the other. In
contrast, with tighter regulation, the agent tends to follow the other by
adjusting its intention. We conclude that the tightness of complexity
regulation crucially affects the strength of the SoA and the dynamics of
interactions between agents.
