Tracking the content and spatio-temporal unfolding of top-down predictions across the cortical hierarchy
Our brain not only processes incoming information from the environment, but also continuously predicts upcoming events. How are these predictions encoded in natural, and thus complex, situations? Which are they neural underpinnings? How do they propagate across the cortical hierarchy? Can we find different types of predictions based on their neural substrates? Using high-temporal resolution methods (MEG and ECoG) and visual narratives, we aim to understand the algorithmic nature of predictions in naturalistic contexts and how they are implemented in the brain, through a variety of MVPA and cognitive modeling techniques.