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

• Subject Area: Human Cognitive and Systems Neuroscience
• Term: from 2016 to 2019
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 261402652

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.

• DFG Programme: CRC/Transregios
• Subproject of: TRR 169: Crossmodal Learning: Adaptivity, Prediction and Interaction
• International Connection: China
• Applicant Institution: Universität Hamburg
• Co-Applicant Institution: Tsinghua University
• Project Heads: Dr. Jan Gläscher; Professor Dr. Jun Zhu