Brain algorithmics: reverse engineering dynamic information processing in brain networks from MEG time series

The ultimate goal of cognitive neuroscience is to understand the brain as an organ of information processing. This will remain difficult unless we understand more directly what information the brain processes when it categorises the external world. For example, our brain can extract from a face (a powerful social communication tool) information to categorise identity, age, gender, ethnicity, emotion, personality and even health. Though our brain knows what information to use for each task, as information receivers we typically do not have direct access to this knowledge. The current state of cognitive neuroscience is similar – we aim to understand the brain as an information processor, but we do not know what stimulus information it processes. Professor Schyns will address this fundamental problem by developing brain algorithmics, a framework that first isolates what specific information underlies a given face categorisation, then examines where, when and how the brain processes this information.