Our group investigates brain systems that are involved in the transformation of experience into enduring memories. Classically, human learning and memory was mainly divided into explicit (with awareness) and implicit (without awareness) memory, but this behavioural characterization cannot simply be mapped to distinct brain areas. For example, we have shown that implicit learning can recruit structures of the medial temporal lobe (MTL) system, which was previously regarded as exclusive to explicit memory. We are using functional magnetic resonance imaging (fMRI) and time-frequency based EEG analysis techniques to examine the systems that contribute to the different aspects of learning and memory. Combined fMRI- EEG experiments are used to describe locations and network dynamics involved in learning and memory. We further incorporate EEG- based Neurofeedback to modulate ongoing activity and examine the consequences for stimulus processing and memory formation.
Signe is a doctoral student who joined the group in April 2014. In her PhD project, she investigates the functional role of pre-stimulus brain activity in memory formation. She examines the nature of oscillatory states that seem to be beneficial for the encoding of new information, how such states can be modulated and whether these modulations can be used to influence encoding success. Methodologically, she is interested in EEG time-frequency analyses, brain-computer-interfaces and combined EEG-fMRI measurements.
Philipp has joined the group at the start of 2013. He's been investigating the relationship between ongoing neuronal activity and the variability of perceptual classifications (e.g. pain) using BCI-based neurofeedback. Currently, he is focusing on the crossmodal transfer of information in implicit learning, also employing BCI-based methods in EEG and EEG-fMRI. Further interests of his include the limitations of perception, psychophysics and machine learning.
Julia is a doctoral student who joined the group in April 2016. In her PhD project, she investigates how crossmodal predictions can be achieved implicitly and the underlying transition from implicit to verbally reportable (explicit) knowledge. Based on fMRI and EEG measurements, she investigates the neurological mechanisms associated with implicit learning of crossmodal predictions in order to detect the underlying different indicators of crossmodal integration and prediction of implicit knowledge. Furthermore, she is interested in brain-computer-interfaces, the study of consciousness and affective neuroscience.