Information about MCNB and its modules, followed by an FAQ section below.

Study regulations can be found on the MCNB examination office website.

MCNB

Degree: Master of Science (M.Sc.) in Cognitive Neuroscience

Applicant Intake: Limited number of highly motivated students

Program Start: Mid-October

Program Duration: 4 semesters, including M.Sc. thesis

Language of Instruction: English

Tuition Fees

• There are no designated tuition fees for this Master program.
• For enrollment, students will face administrative costs of approximately 310 - 320 € per semester.
• Funding and scholarships can be obtained upon personal engagement at common institutions and societies.

Content/Aims

• The Master's program is integrative and research-focused. The Cognitive Neuroscience Master qualifies for scientific work in the fields of fundamental and applied research with neurocognitive methods.
• Students gain a broad theoretical and methodological competence in analyzing and predicting the neurocognitive principles of experience and behavior.
• The Cognitive Neuroscience Master offers excellent qualification for an academic career in the fields of general and neurocognitive psychology, and biological psychology and cognitive neuroscience.

Helpful Prior Knowledge

• Basics in mathematics and statistics
• Basics in neurobiology / medicince / biological psychology
• Basics in programming / computer science / data science
• Basics in cognitive psychology
• Experience in quantitative empirical research

 

Associated Reseach Groups

• Neurocomputation and Neuroimaging (Felix Blankenburg)
• Neural Dynamics of Visual Cognition (Radoslaw Cichy)

Partner Institute

• Center for Cognitive Neurosciences Berlin (CCNB)

Other Research Institutes in Berlin

• Einstein Center for Neurosciences Berlin
• Max Planck Institute
• Charité (Berlin Mitte)
• Berlin Center for Advanced Neuroimaging (BCAN)
• Mind and Brain School (M&B)
• Bernstein Center for Computational Neuroscience (BCCN)
• HERTIE
• Helmholtz Institutes
• Leipnitz Institutions
• Einstein Center for Neurosciences Berlin (ECN) 
• MDC
• ...

Overview of Modules

For the structure of the studies and the program please take a look at the OSA. Course catalog can be find here. Please specify the current semester and the department name (Education and Psychology). More detailed information on modules can be found at the end of the study and examination regulations document.

Cognitive Neuroscience: Perception, Attention, Action, and Cognitive Control

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.

Cognitive Neuroscience: Memory, Emotion, Language, and Consciousness

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).

Cognitive Neuroscience: Research Practice

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.

Neurocognitive Methods and Data Analysis

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.

Probabilistic and Statistical Modelling

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.

Introduction to Programming

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.

Neurocognitive Methods Practical

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.

Research Workshop

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. 

Research Experience

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.

Applied MRI/fMRI: Data Modeling

Building upon the module “Neurocognitive Methods and Data Analysis,” students develop and apply their methodological, analytical, and data modeling skills for processing data from MRI/fMRI studies.

Applied MRI/fMRI: Advanced Data Modeling

Building upon the modules “Neurocognitive Methods and Data Analysis” and “Applied MRI/fMRI: Data Modeling,” students develop and apply their methodological, analytical, and data modeling skills for processing data from MRI/fMRI studies at a more complex, advanced level.

Applied EEG: Data Modeling

Building upon the module “Neurocognitive Methods and Data Analysis,” students develop and apply their methodological, analytical, and data modeling skills for processing data from EEG studies.

Applied EEG: Advanced Data Modeling

Building upon the modules “Neurocognitive Methods and Data Analysis” and “Applied EEG: Data Modeling,” students develop and apply their methodological, analytical, and data modeling skills for processing data from EEG studies at a more complex, advanced level.

Applied Cognitive Neuroscience Methods: Data Modeling

Building upon the module “Neurocognitive Methods and Data Analysis,” students develop and apply their methodological, analytical, and data modeling skills for processing data obtained through neurocognitive methods.

Applied Cognitive Neuroscience Methods: Advanced Data Modeling

Building upon the module “Neurocognitive Methods and Data Analysis,” students develop and apply their methodological, analytical, and data modeling skills for processing data obtained through neurocognitive methods at a more complex, advanced level.

Applied Cognitive Neuroscience

Building upon the module “Cognitive Neuroscience: Research Practice,” students deepen their knowledge of current research in a specific field of cognitive neuroscience (e.g., general psychology, biopsychology, social neuroscience, affective neuroscience, developmental psychology, developmental neuroscience, practical implementation of good scientific practice and open science). The exact content of the module are flexible and adapted to current research topics and trends.

Applied Theoretical Neuroscience

Building on the contents of the modules “Neurocognitive Methods and Data Analysis” and “Probabilistic and Statistical Modeling,” students examine current theoretical models of neural processes (e.g., simulation of neuron behavior, mathematical formulation of neural networks) and put these into practice in relation to their mathematical formulation and simulation in current research.

Applied Computational Cognitive Neuroscience

Building upon the contents of the modules “Neurocognitive Methods and Data Analysis” and “Probabilistic and Statistical Modeling,” students gain practical experience with current analysis and modeling methods in Computational Cognitive Neuroscience, such as Markov decision processes, partially observable Markov decision processes, reinforcement learning, drift-diffusion reaction time models, biophysical network models, and neural networks.

Master Thesis

In this module students complete their Master's Thesis project. The MCNB program offers students a number of options for their theses projects with the requirement that students gain some expertise within a neuroscientific domain. The projects can, but do not have to, take advantage of a neuroimaging technique such as EEG or fMRI. Students should discuss their ideas with professors of the MCNB program before committing to a project to ensure that their project fulfills the MCNB program MSc thesis requirements.

Frequently Asked Questions (FAQ)