Abstract Many behaviors operate in a Bayesian framework, where actions are guided by a complex interaction between current sensory information and past experience, or “priors”. When current sensory information is weak, it is advantageous to allow the prior to guide behavior. However, the value of prior experience lessens as sensory information strengthens. Our goal is to determine how Bayesian behavior arises from the operation of a neural circuit. Smooth pursuit eye movement is an example of a relatively simple sensory-motor behavior that operates in a Bayesian-like manner. Smooth pursuit is a visually-guided voluntary eye movement that can be separated into two primary components: visual drive and visuomotor gain. It is appealing to think of these two components from a Bayesian perspective: the visual drive arises from the middle temporal visual area (MT) and represents the likelihood function derived from sensory data; the visuomotor gain is controlled by the smooth pursuit region of the frontal eye field (FEFsem) and represents the prior. The two concepts and their neural instantiations are integrated within the pursuit system to drive the ultimate eye movement. Both components have been studied independently, but our goal is to study their integration. The dorsolateral pontine nucleus (DLPN) and the nucleus reticularis tegmenti pontis (NRTP) both receive convergent input from cortical areas of pursuit and have cells with a range of visual and visuomotor signals. Therefore, we will record from single neurons in the DLPN and NRTP of awake behaving rhesus monkeys to study the integration of visual and visuomotor signals in the pursuit system. To better understand the relationship between the integration of visual and visuomotor signals and the emergence of Bayesian-like behavior, we have developed a behavioral paradigm that allows us to rapidly adapt priors for target speed. We can control tightly the adaptation of the prior by adjusting the statistics of the target speed, and we can control the expression of the prior by adjusting the strength of the visual motion signals. This will reveal how the integration of visual and visuomotor signals in the pons changes as a function of the state of the prior and the strength of sensory information. To directly study the role of FEFsem in the integration of these signals, we will pair our pontine recordings with simultaneous recordings of multiple single units in the FEFsem. Neuron-neuron correlations between the responses of pontine and FEFsem neurons will reveal functional connectivity between the areas and move towards a description of how the pursuit circuit works as a system. By gaining a better understanding of how the pursuit system integrates priors and sensory information, we will develop general principles of sensory-motor brain circuits.