Bayesian Integration in Sense of Agency: Understanding Self-attribution and Individual Differences

Abstract This study developed a Bayesian integration model to explore individual differences in the sense of agency, particularly when multiple sensory cues influence action perception. Behavioral results showed that individuals consistently integrate sensory cues in agency judgments, though cue weighting varies significantly across individuals. The estimated parameters of our model successfully captured these differences, reflecting individual sensitivities and criteria for agency. Higher sensitivity to sensory inputs was associated with lower variance in the likelihood distribution under the assumption of control. Our model provides critical insights into the mechanisms of the sense of agency and its variability, highlighting the model’s potential for understanding both typical and disordered agency experiences. Competing Interest Statement The authors have declared no competing interest.