Master
2024/2025
Research Seminar "Cognitive Sciences"
Type:
Compulsory course (Cognitive Sciences and Technologies: From Neuron to Cognition)
Area of studies:
Psychology
Delivered by:
Institute for Cognitive Neuroscience
When:
1 year, 1-4 module
Mode of studies:
offline
Open to:
students of one campus
Master’s programme:
Cognitive Sciences and Technologies: From Neuron to Cognition
Language:
English
ECTS credits:
6
Contact hours:
80
Course Syllabus
Abstract
Research Seminar "Cognitive Sciences" (2nd year) is dedicated to advanced data analysis of neurophysiological data and provides the understanding of algorithmic pipelines routinely used in the analysis of EEG and MEG data. Given the quick development of analysis tools, it is always challenging to fully comprehend the machinery hidden behind the typical button-press toolbox packages. Instead of approaching data analysis packages as a “black box”, at the end of the course the students will be able to fully comprehend the meaning of their choices while setting options in their data analysis workflow. During this course, we will go through the details of data acquisition, data processing and step by step implementation of most advanced data analysis pipeline and the understanding of the main parameters involved. After quickly reviewing the physical principles of signal acquisition and introducing some mathematical tools, the course dives into the main topics of time-frequency analysis, source reconstruction, functional connectivity and statistical analysis. The course provides the students with the basic theory of neurophysiological data analysis which is useful not only in neuroscience and cognitive sciences but also in other scientific areas using similar mathematical framework.
Learning Objectives
- Analysis in the time-frequency domain
- This course aims to teach students fundamental steps of the scientific method with practical applications that the students can use with their own thesis topics. For example, writing an abstract about their thesis topic, developing their data analyses plan and preparing a presentation for the thesis project. This course will offer students the opportunity to study how research examines how the mind works. This endeavour requires knowledge drawn from multiple perspectives. The lecturer will employ perspectives from psychology and neuroscience to explore the nature of mental processes. Students will have the opportunity to discuss how their own thesis project can contribute to the literature.
- Reconstruction of neurophysiological sources
- Research methods and experimental design are fundamental aspects for properly prepared scientific projects focusing on practical aspects. The lecturer will present on hypotheses development, various methodologies and tools used to answer different questions in cognitive science and psychological meta-subjective task analyses.
- Lectures will focus on brain areas and related functional properties. Students will engage in practical activities that target cortical and sub-cortical regions. Practical activities will include historical understanding and current findings related to specific brain areas.
- Use of naturalistic stimuli in cognitive stidues
- Connectivity analysis methods
- The lecturer will overview fundamental practices in data collection, highlight the importance of hypothesis appropriate statistical analyses and introduce tools for analyzing data.
- Knowledge translation is key for communicating research findings. Academic writing may vary from short abstracts to long monographs. The lecturer will present on various writing techniques and give tips for academic writing focusing on research reports for peer-reviewed scientific journals. Practical activities will include preparing text for knowledge translation such as writing conference abstract.
- Statistical analysis of EEG/MEG data
- The last weeks of this course will focus on skills and techniques for orally presenting scientific findings. The lecturer will overview the dos and don'ts of poster and paper oral presentations.
- The aim is to help students choose a supervisor, a laboratory, better variant for research project and methods
Expected Learning Outcomes
- Know basic physical principle of signal acquisition
- Know how to approach the statistical evaluation of analysis outcome
- Know how to build a time-frequency projection of EEG/MEG data
- Know how to compute and interpret connectivity analysis
- Know how to track neural sources recorded by EEG/MEG
- Students will choose a supervisor for scientific work, a laboratory, research methods.
- Students will learn about the brain by actively participating in presentations on historical and contemporary knowledge on brain areas of interest.
- Students will learn write conference abstracts, the first and subsequent paragraphs of an introduction and how to structure the content of a scientific paper.
- Students will present their own thesis projects with an oral presentation and learn how to give and receive constructive feedback on their work.
- The students will engage in discussion and activities associated how we study the brain and mental processes using the scientific method.
- The students will learn about good practices on data collection, analyses and software tools and have the opportunity to discuss practical aspects in preparing their own research projects.
- The students will think critically about experimental methodology related to their own experiment. They will complete a freely available research ethics course that covers ethical contact for research involving humans.
Course Contents
- Cognitive science as a field of science
- Starting a research project
- Sampling and data collection
- Logic, Probability, and Statistics
- Experiments and quasi-experimental and non-experimental plans
- Statistical analysis of multidimensional data: non parametric testing
- Data organization and visualization
- Statistical methods 1: t-test, regression, repeated-measure ANOVA, Chi-square-test
- Statistical methods 2: Multivariate-analysis
Assessment Elements
- Year 1, Module 1-2: Two essays
- Year 1 (2023/2024 year) - 3-4 modules - final test
