介绍多感觉通道时间感知以及校正的范式和研究，基于卡尔曼滤波的动态贝叶斯建模，对多感觉通道领域的时间感知（包括同时性、时距以及时间先后关系）进行统一的刻画。

英文摘要：

In the recent years we have seen a significant progress in our understanding of human multisensory perception.  One of the advances came from the application of Bayesian Decision Theory (BDT) to the modeling of multisensory integration and recalibration. For example, is has been shown that the integration of redundant multisensory information can be well described using a maximal-likelihood-estimation model, which can be derived from the Bayesian framework. Other work has shown that prior knowledge about the statistical regularities of the word is integrated with sensory information in a way consistent with the Bayesian approach. And yet other work has demonstrated that multisensory learning and recalibration can be described by dynamic statistical models derived from the Bayesian framework such as the Kalman-Filter. However, most of the work in this area is concerned with the spatial aspects of human multisensory perception. Recently we have started to apply the same statistical models to multisensory phenomena in the temporal domain as well. In this talk I will outline how we can use the Bayesian statistical framework to understand multisensory integration and recalibration of temporal features such as simultaneity, duration, and temporal order.

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