Bayesian optimal adaptation explains age-related human sensorimotor changes

The brain uses information from different sensory systems to guide motor behavior, and aging is associated with a simultaneous decline in the quality of sensory information provided to the brain and a deterioration in motor control. Correlations between age-dependent decline in sensory anatomical structures and behavior have been demonstrated, and it has recently been suggested that a Bayesian framework could explain these relationships. Here we show that age-dependent changes in a human sensorimotor reflex, the vestibulo-ocular reflex, are explained by a Bayesian optimal adaptation in the brain occurring in response to death of motion-sensing hair cells. Specifically, we found that the temporal dynamics of the reflex as a function of age are predicted (r=0.93, p<0.001) by a Kalman filter model which determines the optimal behavioral output when the sensory signal-to-noise characteristics are degraded by death of the transducers. These findings demonstrate that the aging brain is capable of generating the ideal and statistically optimal behavioral response when provided with deteriorating sensory information. While the Bayesian framework has been shown to be a general neural principle for multimodal sensory integration and dynamic sensory estimation, these findings provide evidence of longitudinal Bayesian processing over the human lifespan. These results illuminate how the aging brain strives to optimize motor behavior when faced with deterioration in the peripheral and central nervous system, and have implications in the field of vestibular and balance disorders, as they will likely provide guidance for physical therapy and for prosthetic aids that aim to reduce falls in the elderly.

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