(Springer, 21.08.2019) Schürmann, Tim; Vogt, Joachim; Christ, Oliver; Beckerle, Philipp
Bayesian cognitive modeling has become a prominent tool for the cognitive sciences aiming at a deeper understanding of the human mind and applications in cognitive systems, e.g., humanoid or wearable robotics. Such approaches can capture human behavior adequately with a focus on the crossmodal processing of sensory information. We investigate whether the Bayesian causal inference model can estimate the proprioceptive drift observed in empirical studies.