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Structure of the studies
The four-semester M.Sc. program consists of ten modules and a Master's thesis (120 ECTS). The program is divided into two phases: Study and Research, corresponding to the two years of the program.
During the first year, the Study phase, the students are expected to complete courses covering the program's core themes. The study phase aims to provide an in-depth theoretical introduction to the three research themes and equip the students with appropriate methodological skills for the Research phase.
In the Research phase the students will have the opportunity to pursue their own research project under the supervision of the program’s faculty members. In the program's second year, the class schedule is reduced to a week of class or one or two sessions a semester.
The structure of the Master's program is presented below.
Cognitive Neuroscience MSc
More detailed information regarding each module is available by clicking the respective button.
During this time the students will complete their master's thesis on a topic of their own choosing.
Based on the basic knowledge acquired in the module Neurocognitive Methods and Data Analysis, the module deals with the practical application possibilities of neurocognitive methods. In particular, the application-oriented data collection and practical analysis with standardized methods (SPM, FSL, etc.) are covered. Univariate as well as multivariate analyses of fMRI data and EEG data are discussed in detail and methods for the analysis of structural and functional connectivity are presented. The practical application of the analysis procedures as well as the interpretation of the resulting results against the background of scientific initial hypotheses and theories will be explicitly practiced.
In accordance with current developments in cognitive neuroscience, students hone their practical skills in programming with RStudio, MATLAB, Python, or similar programming languages – skills that are currently highly sought after. They gain practical experience in managing empirical data and analysis methods, building on the theoretical introduction to this they have gained in the modules “Neurocognitive Methods and Data Analysis” and “Probabilistic and Statistical Modeling.” The focus is on the application of imperative programming in neurocognitive research. In particular, students practice the implementation of scripts for stimulus presentation (e.g., precise presentation of visual stimuli), data acquisition (e.g., response behavior, reaction times), data visualization, and statistical evaluation (e.g., output of charts, calculation of inferential statistics). Additionally, principles of data management (e.g., management of research data) in accordance with good scientific practice, as well as the cooperative use of development platforms (e.g., Github) and principles of publication and the availability of programming code in the sense of open science, are also practiced.
In this module, you choose three courses between the following:
- Applied MRI/fMRI: Data Modeling
- Applied MRI/fMRI: Advanced Data Modeling
- Applied EEG: Data Modeling
- Applied EEG: Advanced Data Modeling
- Applied Cognitive Neuroscience Methods: Data Modeling
- Applied Cognitive Neuroscience Methods: Advanced Data Modeling
- Applied Cognitive Neuroscience
- Applied Theoretical Neuroscience
- Applied Computational Cognitive Neuroscience
This module makes use of review articles and advanced literature to provide an overview of current neurocognitive methods and typical experimental designs . Basic aspects of neurophysiology and M/EEG signal generation, recording, and analysis are taught. An introduction to fMRI is provided through use of a textbook and further literature, and basic aspects of fMRI signal generation, recording, and analysis also form part of the curriculum. Students analyze M/EEG and fMRI datasets and create analysis scripts for data processing.
The research internship takes place in a domestic or foreign research institution under the guidance of an experienced scientist. The possible fields of application are very diverse and lie within the entire spectrum of neuroscientific research. Students are actively involved in the research process and participate in the theory-driven design, planning, execution, statistical analysis, interpretation and experimental or theoretical/simulation-based studies.
The module supports students in learning to classify the content of the modules Cognitive “Neuroscience: Perception, Attention, Action and Cognitive Control,” “Cognitive Neuroscience: Memory, Emotion, Language and Consciousness,” and “Neurocognitive Methods and Data Analysis” within a theoretical framework and to evaluate these at a fundamental scientific level. Specifically, the basics of neuroanatomy and current research on the structure of the nervous system are covered in terms of their application. Techniques of good scientific practice, scientific ethics, open science, and scientific writing and presentation of results are addressed. The research approaches that are particularly relevant to practice and their suitability for testing specific hypotheses are critically discussed. Practice sessions allow for the validation of theoretical models, as well as the interconnections between research approaches, to be critically discussed. The specific content of this module will be adapted to reflect the latest developments in current research. Expert lectures on current research findings will be prepared and critically reflected upon based on research reports, for example, from the fields of cognitive neuropsychology, computational neuroscience, theoretical neuroscience, social and affective neuroscience, as well as methodological developments in analysis methods and areas of application focused on data science.
With a background in current neurocognitive theories and hypotheses, students will develop their own research questions in the social, cognitive and affective neurosciences and present them orally and in writing. They will also practice and critically reflect on the methodological and interpretative principles necessary for their empirical verification.
Students are provided with an introduction to theoretical foundations and important empirical findings from the field of cognitive neuroscience and other related fundamental subjects (such as general and biological psychology) through selected examples. Students gain an overview of the mutually beneficial use of selected neurocognitive methods in conjunction with algorithmic process models and their practical applications. The sensory physiology of vision, hearing, chemical senses, and the somatosensory system are presented and discussed at a level that focuses on their neurophysiological description, with review articles used to present these in relation to subcortical and cortical information processing. Types of attention and their neural mechanisms, as well as the bases of action, decision-making, and cognitive control mechanisms, are presented and discussed based on current review articles.
Selected theoretical foundations and important empirical findings from cognitive neuroscience and related foundational disciplines (e.g., general and biological psychology) are conveyed through selected examples. Students are introduced to the benefits of using selected neurocognitive methods in conjunction with algorithmic process models and their practical applications. The distinction between the memory processes of short-term and long-term memory as well as encoding and retrieval of memory content, the underlying neurobiological processes, and their neuroanatomical classification are also discussed. The neurobiological principles of emotion and language processing and production, as well as their contributions to cognitive processes such as decision-making, are discussed based on review articles. The challenges in defining and operationalizing concepts in cognitive neuroscience are debated based on current research into human consciousness and current research on neural correlates of conscious processes (e.g., sub- and supraliminal stimulus processing, disorders of consciousness, conscious contents, and altered states of consciousness).
Building upon the knowledge gained in previous studies, students deepen their understanding of the following topics: correlation and regression, multiple and logistic regression, application of the general linear model and multilevel models, frequentist and Bayesian reasoning with approaches to control error rates (especially type 1 errors). Students gain experience in practically applying their knowledge of multivariate analysis methods using data set examples from cognitive neuroscience while under supervision, and are also able to gain experience with approaches based on machine learning. Advanced methods of neuroimaging data analysis such as biophysical modeling approaches (e.g., psychophysiological interactions, dynamic causal modeling, etc.) are implemented in programming languages such as Matlab, RStudio, or Python using toolbox implementations.