Novel applications of Bayesian inference clarify sensorimotor uncertainty during stepping movements

The human nervous system relies on sensory information from the feet and legs to control the way we balance and walk. However, even in healthy individuals this sensory information is inherently variable and clouded with uncertainty. Researchers have found that the central nervous system (CNS) estimates body position amid the uncertainty of sensory signals in a way consistent with Bayesian inference. Bayesian inference posits that the brain accounts for variability in sensory data by combining it with learned expectations built from previous movement attempts. While initial findings on this topic are promising, they have neglected to study full-body movements such as gait and balance. The purpose of this research was to determine if the CNS controls balance-related stepping tasks in a way that fits a Bayesian framework. To address this purpose, we created a virtual reality protocol where participants moved their center of mass (CoM) to various targets while relying on uncertain visual cues and compensating for an alternating shift to the cursor position. We showed that as incoming sensory information became less certain, participants relied more on their learned expectation of body position and demonstrated more uncertainty in their responses. Accordingly, as participants learned to control and estimate their CoM position during our mobility task, they relied both on the sensory information they were receiving as well as learned expectations for its location. These results provide further evidence that the CNS is aware of the variability in sensory information and is proficient at compensating for the resultant uncertainty. We aim to apply these findings as a method for measuring the efficacy of interventions targeting sensory function.