A Bayesian psychophysics model of sense of agency

Despite the increasing significance of sense of agency (SoA) research, the literature lacks a formal model: what computational principles underlie SoA, the registration that oneself initiated an action that caused something to happen? We theorize SoA in the framework of optimal Bayesian cue integration with mutually involved principles, namely, reliability of action and outcome sensory signals, their consistency with the causation of the outcome by the action, and the prior belief in causation. We used our Bayesian model to explain the intentional binding effect, hailed as reliable indicator of SoA. Our model explains temporal binding in both self-intended and unintentional actions suggesting that intentionality is not strictly necessary given high confidence in the action causing the outcome. Our Bayesian model also explains that if the sensory cues are reliable, SoA can emerge even for unintended actions. Our formal model therefore posits a precision-dependent causal agency.