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The Bayesian causal inference model benefits from an informed prior to predict proprioceptive drift in the rubber foot illusion
[article]
2019
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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.
doi:10.26041/fhnw-1901
fatcat:lpob3mncyrcsdoekafsek6ffoy