主题：Merging the Senses in Time
报告人：Prof. Marc Ernst, Applied Cognitive Psychology, Ulm University
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.