Prior knowledge facilitates our perception and goal-directed behaviors in the dynamic world, particularly when sensory input is lacking or noisy. However, the neural mechanisms underlying how prior expectation improves sensory-motor behaviors remain unknown. Herein, we examined neural activity in the middle temporal visual area (MT) while monkeys perform a smooth pursuit eye movement task with the prior expectation of the visual target's motion direction. We found that prior expectation discriminately reduced the MT neural responses depending on their preferred directions, only when the sensory evidence was weak. This response reduction effectively sharpens neural population direction tuning. Furthermore, the state-space analysis showed the task-specific prior expectation signals in population activity. In-silico simulations with a realistic MT population demonstrated that the sharpening of the tuning fully explained the biases and variabilities in smooth pursuit, suggesting neural computations in the sensory area alone can underpin the integration of prior knowledge and sensory evidence.

Humans integrate multiple sources of information for action-taking, using the reliability of each source to allocate weight to the data. This reliability-weighted information integration is a crucial property of Bayesian inference. In this study, participants were asked to perform a smooth pursuit eye movement task in which we independently manipulated the reliability of pursuit target motion and the direction-of-motion cue. Through an analysis of pursuit initiation and multivariate electroencephalography activity, we found neural and behavioral evidence of Bayesian information integration: more attraction toward the cue direction was generated when the target motion was weak and unreliable. Furthermore, using mathematical modeling, we found that the neural signature of Bayesian information integration had extra-retinal origins, although most of the multivariate electroencephalography activity patterns during pursuit were best correlated with the retinal velocity errors accumulated over time. Our results demonstrated neural implementation of Bayesian inference in human oculomotor behavior.

Prior knowledge facilitates our perception and goal-directed behaviors, particularly when sensory input is lacking or noisy. However, the neural mechanisms underlying the improvement in sensorimotor behavior by prior expectations remain unknown. In this study, we examine the neural activity in the middle temporal (MT) area of visual cortex while monkeys perform a smooth pursuit eye movement task with prior expectation of the visual target’s motion direction. Prior expectations discriminately reduce the MT neural responses depending on their preferred directions, when the sensory evidence is weak. This response reduction effectively sharpens neural population direction tuning. Simulations with a realistic MT population demonstrate that sharpening the tuning can explain the biases and variabilities in smooth pursuit, suggesting that neural computations in the sensory area alone can underpin the integration of prior knowledge and sensory evidence. State-space analysis further supports this by revealing neural signals of prior expectations in the MT population activity that correlate with behavioral changes.