We contribute to teaching in the MSc program Social, Cognitive, and Affective Neuroscience with the following modules:
The aim of the module “Statistical Methods” is to familiarize the student with the theoretical foundations of data-analytical strategies employed in cognitive neuroscience. It comprises five subunits: a mathematical precourse, an introduction to the general linear model and its applications, an introduction to Fourier analysis, an introduction to the GLM as applied in functional magnetic resonance imaging (FMRI), and an introduction to an FMRI model class referred to as “Dynamic Causal Models” (DCMs). The module extends over two semesters, starting in the winter term. The principle reading material for the module is the lecture script, which can be downloaded below.
Neurocognitive Methods and Programming
The aim of the module is to familiarize students with the essential theoretical background knowledge for the practical implementation and evaluation of neuroscientific studies. Students will be able to discuss the relative limitations and benefits of various neurocognitive methods such as EEG and FMRI. Furthermore, they will gain theoretical knowledge in, and practical experience with, imperative programming. The module combines a lecture series with practical exercises and a final project.The module comprises four sections. (1) An introduction to electroencephalography (EEG), covering fundamentals of the EEG technique, including its physiological basis, data acquisition, and data analysis methods, (2) an introduction to functional magnetic resonance imaging (FMRI), covering fundamentals of the FMRI technique, including its physiological basis, data acquisition, and data analysis methods, (3) an introduction to imperative programming using Matlab in theory and praxis, and (4) an introduction to neurocognitive experiment programming using the Cogent and Psychtoolbox Matlab Toolboxes.