Bayesian analysis of the interaction of learning, semantics and social influence with crossmodal integration (B02)

项目将使用贝叶斯分析的新方法来研究人类受试者的跨模态刺激的整合如何与学习，语义和社会背景相互作用。正则化贝叶斯推理提供了一种优雅的方式，将调节影响纳入贝叶斯模型。使用这种技术，该项目将能够对行为和功能磁共振成像数据进行建模，不仅可以描述学习、语义和社会背景如何调节跨模态整合，还可以描述这种整合如何影响（促进或阻碍）学习和决策。

Project will use novel methods of Bayesian analysis to investigate how the integration of crossmodal stimuli in human subjects interacts with learning, semantics and social context. Regularized Bayesian inference provides an elegant way to incorporate modulatory influences into Bayesian models, Using this technique, the project will be able to model behavioural and fMRI data and describe not just how learning, semantics and social context can modulate crossmodal integration but also how this integration can impact (facilitate or impede) learning and decision making.