Our group is interested in characterising the neural correlates of perceptual and value-based decision making in humans, including reinforcement-guided learning and reward-related activity in the dopaminergic system. The computational techniques used in our lab are motivated by classical problems in signal processing, machine learning and statistical pattern recognition. Our ultimate goal is to go beyond mere "brain mapping" and begin looking for distributed neural representations to decipher how information flow through a "network" can lead to changes in behaviour. To this end we use a multimodal approach, which combines various forms of neuroimaging (EEG/MEG, fMRI, simultaneous EEG-fMRI & EEG-pupilometry) and interventional techniques (TMS/tDCS) along with computational modelling and multivariate data analysis techniques to expose the relevant brain networks and their underlying computations.
My talk is entitled: Dynamic network reconstruction of human decision making and learning via EEG-fMRI fusion