Od roku akademickiego 2019/2020 studia II stopnia z kognitywistyki prowadzone są w języku angielskim pod nazwą Cognitive Science. Na tej stronie znajdują się krótkie opisy przedmiotów przewidzianych programem studiów.
Semester I
Advanced topics in cognitive science
The course is aimed to familiarize participants with the current trends in research and controversies in cognitive science. The course will help students (1) broaden their knowledge of cognitive processes and their cerebral foundations, (2) clarify their own research interests, choose their thematic path (neurocognitive or computational) and master’s research topic. The course begins with an outline of current controversies around the architecture of cognition (symbolic vs. embodied cognition, network organization of the system – computational and neurobiological issues). Further, some methodological issues will be taken up, as well as the issues of elementary representations and cognitive processes which enable the orientation in the physical and social environment. Some of the lectures will be co-led by specialists in a given field.
Introduction to programming in Python (elective)
This is an introductory course to computer programming in Python. It does not require any previous programming experience. In the class students learn to think algorithmically and decompose problems into manageable parts. Through simple examples they become familiar with programming concepts such as variables, conditional statements, loops, functions, recursion. Basic Python syntax, standard data structures and flow control statements are introduced. Procedural style of programming is encouraged.
Advanced Python for cognitive scientists
The goal of the course is to build fluency in using Python programming language as a tool for scientific computing, data manipulation and visualization. We will introduce libraries which constitute a core of Python ecosystem for data analysis: numpy, scipy, pandas, matplotlib. After covering the basics, students will have the opportunity to hone their skills by working through a number of applications of the introduced tools in data analysis. Simultaneously, they will be improving their programming style and learning about good programming practices. Previous experience with Python is necessary.
Cognitive processes modelling I
Cognitive systems are characterized by their ability to functionally adapt to their environments, which in turn allows them to react to the changes in their surroundings accordingly or initiate actions of their own. Mechanisms of functional adaptation of this kind are found in a wide variety of phenomena spanning multiple scales: biological systems (single cells, cell colonies, organized tissues, systems such as immune system etc.), whole organisms, higher animals and humans with their mental processes, social groups exhibiting cultural adaptation, and artificial systems (autonomous robots, software agents). Modeling such phenomena requires an interdisciplinary approach in which different fields of study stimulate each other: psychological and biological discoveries inspire the development of new mathematical models and computational methods, which often find applications outside of the original domain. Developed models help to formulate the hypotheses, plan further experiments, verify theories, and augment the overall understanding of cognitive processes.
The aim of this course is to give an overview of various paradigms, approaches and methods used to model processes of systemic adaptation. We show how different methods relate to each other and how they can be applied to uncover different aspects of studied phenomena. We focus on methodological issues and illustrate them with examples of concrete models and concrete research from multiple domains such as motor development, decision making, language acquisition, social coordination, cultural evolution etc.
Introduction to neuroanatomy (elective)
This course is an introduction to the nervous system with a neuroanatomical emphasis. We will study the structure and function of the human central and peripheral nervous systems. General neuroanatomy topics will include the gross and microscopic structure, neurophysiology of the brain, spinal cord and nerves with descriptions of alterations in normal anatomy through disease or injury.
Methods in neuroscience
During the lecture students learn about different methods of neuronscience and psychophysiology used in basic and applied research. They will learn about the techniques of transcranial stimulation and structural and functional imaging, as well as the advantages and disadvantages of each of these techniques. In addition, the basics of measurement and analysis of the most popular signals used in neuroscience and psychophysiology (e.g. EEG, BOLD, ECG, etc.) and the basic principles of verification of research hypotheses in neurocognitive science will be discussed. Students will also learn which research methods, techniques and procedures should be applied depending on the purpose of the study and the population and how to verify research hypotheses in various fields of psychology using neuroscience and psychophysiology methods.
Modern syntax (elective)
The aim of this course is to present modern syntactic theories. The main three approaches covered in some detail are: Categorial Grammars (stemming from the work of Kazimierz Ajdukiewicz), Dependency Grammars (originating in the work of Lucien Tesnière, but its modern and computationally-oriented version – Universal Dependencies – will be covered in some detail in the course) and Formal Grammars (usually associated with Noam Chomsky). Some emphasis will be put on modern constraint-based theories which build on these three general approaches, namely, on Lexical Functional Grammar (Joan Bresnan and Ron Kaplan) Head-driven Phrase Structure Grammar (Carl Pollard and Ivan Sag).
Modern semantics (elective)
This course covers – in some formal detail – compositional semantics, i.e., the principles governing the composition of meanings of larger syntactic units (sentences, phrases) from the meanings of the constituents (phrases, words) of these units. The course is partially based on the “Semantics in Generative Grammar” textbook (Irene Heim and Angelika Kratzer) and makes use of the “Lambda Calculator” tool (http://lambdacalculator.com/).
Semester II
Introduction to machine learning
This course provides an overview of machine learning concepts and algorithms. It focuses mostly on techniques related to classification and regression, such as nearest neighbors methods, generalized linear models, tree-based methods, support vector machines, feed- forward neural networks. Simple clustering techniques (k-means clustering, hierarchical clustering) are also introduced. Lecture covers main principles behind different algorithms, model evaluation strategies and basics of statistical learning theory. Connections with topics known from cognitive modeling (e.g., categorization models, signal detection theory) or statistics (e.g., sampling, probability density estimation, logistic regression) are made. During laboratory classes students learn practical applications of the introduced methods using libraries from Python ecosystem (scikit-learn, XGBoost, PyTorch).
Advanced statistical methods and models in experimental design
The course assumes students have the basic knowledge of statistical analysis for empirical sciences, including the understanding of the logic of statistical inference, classical statistical tests (t test, chi-square test etc.), and the rudiments of the General Linear Model (ANOVA, simple linear regression). Students without the necessary prerequisites will be offered placements in supplementary courses in the first semester. Based on these foundations, students in this course will learn more advanced statistical methods used in cognitive research: logistic regression, mixed effects models, structural equation modelling, and other extensions of GLM. The course will provide students with hands-on experience with real data analysis using R, a cutting-edge statistical environment.
Cognitive processes modelling II (computational path)
The course consists in more detailed analyses of concrete models of cognitive processes (broadly understood). The processes concern levels of individual cognition, interindividual coordination as well as group processes. The phenomena modeled include categorization, attention, information integration, decision-making and the emergence of communication and language. Lectures are devoted to explaining the suitability of various computational models for those levels and phenomena. Lab work provides hands-on experience in using concrete methods and architectures.
Introduction to natural language processing (computational path)
Natural language processing (NLP) is one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. This course presents different ways of describing the expressions of natural language (English, Polish…) on different linguistic levels (including syntax and semantic) and with the use of various formalisms. It presents the most important existing linguistic resources that can be used in the development of new applications, as well as the existing programming tools allowing for basic linguistic analysis of the text. Various types of applications running on text data will also be presented: information mining, names recognition, terminology extraction or machine translation.
Research methods and experimental design in neuroscience (neurocognitive path)
The aim of the course is to provide basic knowledge about the application of experimental methods of neurocognitive science and to develop the ability to use these methods in practice. It includes lectures combined with basiv training during which students become familiar with the equipment and software enabling them to collect and analyze behavioural, and neuroimaging data (family of MRI and NIRS methods). Students will learn to collect experimental data on their own, as well as to process and analyze them. Analytical exercises will also include working with existing sets of experimental data.
Modern topics in neuroscience (neurocognitive path)
Cognitive neuroscience is a multidisciplinary field which main focuses on exploring neurobiological underpinnings of behavior by the means of neuroimaging methods. Recently, it has been emphasized that complex models of the human behavior cannot be created without developing methods which integrate data from various neuroimaging methods and synthesizing large scale data which are already publicly available. The course will cover a range of methodological advancements which are believed to be necessary for further progressing the cognitive neuroscience field. The list of topics will include among others: meta-analysis in neuroscience, brain stimulation methods, multimodal neuroimaging, open (neuro)science and online repositories, multivariate analysis and mental state decoding, functional and effective connectivity analysis.
Semester III
Philosophy of science: an overview for Cognitive Science
The subject of philosophy of science is reflection on the nature of empirical sciences, analysis of their structure and methods, reconstruction of their assumptions and development models. The aim of the course will be to familiarize students with the main problems, directions and discussions in the philosophy of science, as well as to relate the discussed issues to the specific situation of cognitive science.
Critical reading and academic writing
One of the key academic skills is a critical analysis of empirical research reports in terms of the research questions undertaken, the intended objectives of the work, the methods used and the conclusions drawn. Combined with advanced academic writing and text composition skills, it is a basic tool in the work of a researcher of cognitive processes and their brain organization. The aim of this course is to develop these skills in such a way that the participant is able to carry out in-depth analysis of scientific publications and evaluation of the research carried out; has acquired in-depth knowledge of the research process and how to report on it in psychology and cognitive science, as well as to evaluate and communicate in writing the value of the research on their own.
Advanced applications of neural networks (deep learning) (computational path)
This class provides students hands-on experience in training modern neural networks architectures, acting as universal feature extractors (deep learning). Specialized feed-forward (convolutional network) and recurrent (long short-term memory networks) architectures are introduced. The material is organized around specific applications concerning topics important for cognitive science, for example image recognition, language modeling, modeling action and perception, cognitive robotics. Students train their own models, and experiment with already published models from various domains. The course uses Python programming language and popular neural network libraries (PyTorch, Keras, TensorFlow).
Introduction to Information Theory (computational path)
The course will outline the theory of information and its practical applications in various fields of science. The aim is to provide solid background for understanding the basic measures of information and show their usefulness in other fields of science (biology, linguistics, neuroscience and social sciences). The second part of the course will be focused on the discussion on possible limitations of information theory as understood by classical Shannonian approaches. We will present contemporary works considering those limitations and the discussion of informational complexity.
Developmental cognitive neuroscience (neurocognitive path)
Infancy and early childhood is a period of most dramatic changes in brain organization. The majority of perceptual, motor and cognitive skills emerge during this period. A large proportion of our knowledge about the world is based on developmental achievements from it. Throughout the course we will look at basic concepts and key studies in the area of Developmental Cognitive Neuroscience. That is, the study of associations between cognitive and brain development, with particular emphasis on changes in functional brain organisation.
Psychophysiology and Eye-Tracking (neurocognitive path)
This workshop will familiarise students with the practical use of most important methods of experimental psychophysiology and eye- tracking (oculography). During the classes the students will not only learn the basics of these methods, but also how to carry out signals registration and analysis, as well as how to interpret the data for the measurements of saccades, fixations and pupil dilation.
Semester IV
Communication Skills
In this course students learn how to clearly communicate complex scientific ideas to general public. They develop both written and oral communication skills. They produce a popular scientific article and create a short, few-minute long film.