Bayesian inference provides a cogent account of how the brain combines sensory information with "priors" based on past experience to guide many behaviors, including smooth pursuit eye movements. We now demonstrate very rapid adaptation of the pursuit system's priors for target direction and speed. We go on to leverage that adaptation to outline possible neural mechanisms that could cause pursuit to show features consistent with Bayesian inference. Adaptation of the prior causes changes in the eye speed and direction at the initiation of pursuit. The adaptation appears after a single trial and accumulates over repeated exposure to a given history of target speeds and directions. The influence of the priors depends on the reliability of visual motion signals: priors are more effective against the visual motion signals provided by low-contrast vs. high-contrast targets. Adaptation of the direction prior generalizes to eye speed and vice versa, suggesting that both priors could be controlled by a single neural mechanism. We conclude that the pursuit system can learn the statistics of visual motion rapidly and use those statistics to guide future behavior. Furthermore, a model that adjusts the gain of visual-motor transmission predicts the effects of recent experience on pursuit direction and speed, as well as the specifics of the generalization between the priors for speed and direction. We suggest that Bayesian inference in pursuit behavior is implemented by distinctly non-Bayesian internal mechanisms that use the smooth eye movement region of the frontal eye fields to control of the gain of visual-motor transmission. Bayesian inference can account for the interaction between sensory data and past experience in many behaviors. Here, we show, using smooth pursuit eye movements, that the priors based on past experience can be adapted over a very short time frame. We also show that a single model based on direction-specific adaptation of the strength of visual-motor transmission can explain the implementation and adaptation of priors for both target direction and target speed.

Both perceptual and motor systems must decode visual information from the distributed activity of large populations of cortical neurons. We have sought a common framework for understanding decoding strategies for visually guided movement and perception by asking whether the strong motion aftereffects seen in the perceptual domain lead to similar expressions in motor output. We found that motion adaptation indeed has strong sequelae in the direction and speed of smooth pursuit eye movements. After adaptation with a stimulus that moves in a given direction for 7 sec, the direction of pursuit is repelled from the direction of pursuit targets that move within 90°of the adapting direction. The speed of pursuit decreases for targets that move at the direction and speed of the adapting stimulus and is repelled from the adapting speed in the sense that the decrease either becomes greater or smaller (eventually turning to an increase) when tracking targets move slower or faster than the adapting speed. The effects of adaptation are spatially specific and fixed to the retinal location of the adapting stimulus. The magnitude of adaptation of pursuit speed and direction is uncorrelated, suggesting that the two parameters are decoded independently. Computer simulation of motion adaptation in the middle temporal visual area (MT) shows that vector-averaging decoding of the population response in MT can account for the effects of adaptation on the direction of pursuit. Our results suggest a unified framework for thinking, in terms of population decoding, about motion adaptation for both perception and action.

We have studied how rewards modulate the occurrence of microsaccades by manipulating the size of an expected reward and the location of the cue that sets the expectations for future reward. We found an interaction between the size of the reward and the location of the cue. When monkeys fixated on a cue that signaled the size of future reward, the frequency of microsaccades was higher if the monkey expected a large vs. a small reward. When the cue was presented at a site in the visual field that was remote from the position of fixation, reward size had the opposite effect: the frequency of microsaccades was lower when the monkey was expecting a large reward. The strength of pursuit initiation also was affected by reward size and by the presence of microsaccades just before the onset of target motion. The gain of pursuit initiation increased with reward size and decreased when microsaccades occurred just before or after the onset of target motion. The effect of the reward size on pursuit initiation was much larger than any indirect effects reward might cause through modulation of the rate of microsaccades. We found only a weak relationship between microsaccade direction and the location of the exogenous cue relative to fixation position, even in experiments where the location of the cue indicated the direction of target motion. Our results indicate that the expectation of reward is a powerful modulator of the occurrence of microsaccades, perhaps through attentional mechanisms.

Under natural conditions, motor learning is instructed by sensory feedback. We have asked whether sensory signals that indicate motor errors are necessary to instruct learning or if the motor signals related to movements normally driven by sensory error signals would be sufficient. We measured eye movements in trained rhesus monkeys while employing electrical microstimulation of the floccular complex of the cerebellum and the smooth eye movement region of the frontal eye fields to alter ongoing pursuit eye movements. Repeated electrical stimulation at fixed times after the onset of target motion and pursuit failed to cause any learning that was retained beyond the time period used to instruct learning. Learning was not uncovered when the target was stabilized with respect to the moving eye to prevent competition between instructive signals created by electrical stimulation and visual image motion signals evoked when stimulation drove the eye away from the tracking target. We suggest that signals emanating from motor-related structures in the pursuit circuit do not instruct learning. Instead, instructive sensory error signals seem to be necessary.