The Bayesian causal inference model benefits from an informed prior to predict proprioceptive drift in the rubber foot illusion

Schürmann, TimVogt, JoachimChrist, OliverBeckerle, Philipp2019-12-122019-12-122019-08-211612-47901612-4782https://doi.org/10.1007/s10339-019-00928-9https://irf.fhnw.ch/handle/11654/30158https://doi.org/10.26041/fhnw-1901Bayesian 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.enRubber Leg IllusionBayesian Cognitive ModelingWearable Robotics100 - Philosophie und PsychologieThe Bayesian causal inference model benefits from an informed prior to predict proprioceptive drift in the rubber foot illusion01A - Beitrag in wissenschaftlicher Zeitschrift447-457