Darlington Timothy R, Tokiyama Stefanie, Lisberger Stephen G
Department of Neurobiology, Duke University School of Medicine, Durham, North Carolina.
Department of Neurobiology, Duke University School of Medicine, Durham, North Carolina
J Neurophysiol. 2017 Aug 1;118(2):1173-1189. doi: 10.1152/jn.00282.2017. Epub 2017 Jun 7.
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.
贝叶斯推理为大脑如何将感官信息与基于过去经验的“先验信息”相结合以指导多种行为（包括平稳跟踪眼球运动）提供了一个有说服力的解释。我们现在证明了跟踪系统关于目标方向和速度的先验信息能非常快速地适应。我们接着利用这种适应来勾勒可能导致跟踪表现出与贝叶斯推理一致特征的神经机制。先验信息的适应会在跟踪开始时引起眼球速度和方向的变化。这种适应在单次试验后就会出现，并在反复接触给定的目标速度和方向历史记录时累积。先验信息的影响取决于视觉运动信号的可靠性：相对于高对比度目标提供的视觉运动信号，先验信息对低对比度目标提供的视觉运动信号的作用更有效。方向先验信息的适应会推广到眼球速度，反之亦然，这表明这两种先验信息可能由单一神经机制控制。我们得出结论，跟踪系统能够快速学习视觉运动的统计信息，并利用这些统计信息来指导未来行为。此外，一个调整视觉运动传递增益的模型可以预测近期经验对跟踪方向和速度的影响，以及速度和方向先验信息之间泛化的具体情况。我们认为，跟踪行为中的贝叶斯推理是由明显非贝叶斯的内部机制实现的，这些机制利用额叶眼区的平稳眼球运动区域来控制视觉运动传递的增益。贝叶斯推理可以解释许多行为中感官数据与过去经验之间的相互作用。在这里，我们通过平稳跟踪眼球运动表明，基于过去经验的先验信息可以在非常短的时间框架内得到适应。我们还表明，一个基于视觉运动传递强度的方向特异性适应的单一模型可以解释目标方向和目标速度先验信息的实现和适应。
