Bayes'd and confused: novel applications of Bayesian inference to better understand sensorimotor uncertainty
Effective motor control relies on accurate sensory information. However, sensory information is inherently variable and clouded with uncertainty. Yet, humans perform motor skills with a high degree of proficiency and reliability. How the central nervous system (CNS) controls motor function amid the uncertainty of sensory signals is not known. Researchers in recent years have suggested that the brain may control movement in a way that can be explained by Bayesian inference. Bayesian inference posits that the most probable outcome is the product of both the currently available data (sensory ...
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