Introduction to cognitive science

General data

Course ID:	1000-2M11WK
Erasmus code / ISCED:	11.3 Kod klasyfikacyjny przedmiotu składa się z trzech do pięciu cyfr, przy czym trzy pierwsze oznaczają klasyfikację dziedziny wg. Listy kodów dziedzin obowiązującej w programie Socrates/Erasmus, czwarta (dotąd na ogół 0) – ewentualne uszczegółowienie informacji o dyscyplinie, piąta – stopień zaawansowania przedmiotu ustalony na podstawie roku studiów, dla którego przedmiot jest przeznaczony. / (0612) Database and network design and administration The ISCED (International Standard Classification of Education) code has been designed by UNESCO.
Course title:	Introduction to cognitive science
Name in Polish:	Wprowadzenie do kognitywistyki
Organizational unit:	Faculty of Mathematics, Informatics, and Mechanics
Course groups:	(in Polish) Przedmioty obieralne na studiach drugiego stopnia na kierunku bioinformatyka Elective courses for Computer Science
ECTS credit allocation (and other scores):	(not available) Basic information on ECTS credits allocation principles: the annual hourly workload of the student’s work required to achieve the expected learning outcomes for a given stage is 1500-1800h, corresponding to 60 ECTS; the student’s weekly hourly workload is 45 h; 1 ECTS point corresponds to 25-30 hours of student work needed to achieve the assumed learning outcomes; weekly student workload necessary to achieve the assumed learning outcomes allows to obtain 1.5 ECTS; work required to pass the course, which has been assigned 3 ECTS, constitutes 10% of the semester student load. view allocation of credits
Language:	English
Type of course:	elective monographs
Short description:	Cognitive science is a fast growing area of contemporary science. Using computational methods of artificial intelligence in modeling the behavior and experimental methods of psychology and neuroscience and other disciplines, cognitive science is oriented towards unraveling one of the key issues of contemporary science, eternally fascinating problem of the nature and functioning of mind and its relation to the body. Cognitive science is developing in an active interaction with computer science and its results are applicable in currently evolving computer science sectors, such as multiagent systems, cognitive robotics, automatic planning, knowledge representation and knowledge engineering.
Full description:	The course is aimed at introducing students to issues of symbolic mental representations in cognitive science. The cognitive science paradigm will be discussed , taking into account how human behavior is explained in terms of information processing. Particular mental representations will be discussed from the perspective of the major debates about the nature of mind, the explanation of the behavior and ways of modeling the action of mind. On mind, machines, algorithms and thinking. What does cognitive science owe to ancient Greeks? What is this thing called Cognitive Science? The Computational - Representational Understanding of Mind (CRUM). Mental representations (cognitive representations) Propositions - cognitive representations based on logic Rules as cognitive representations Concepts as cognitive representations Analogies as cognitive representations Perception: mental images as cognitive representations Wason’s Selection Task - an example of a debate on the explanation of empirical results using various types of mental representations Connectionism – subsymbolic (network based) representations as cognitive representations Summary - review and evaluation of the six approaches to the issue of mental representations Challenges facing cognitive science Evolutionary - computational theory of mind and the future of cognitive science
Bibliography:	Paul Thagard (2005) "Mind. Introduction to Cognitive Science", 2nd Edition, MIT Press. Additionally: Steven Pinker (2009) "How the Mind Works", W.W. Norton & Company, Inc. Edward Nęcka, Jarosław Orzechowski, Błażej Szymura (2013) "Cognitive Psychology" (in Polish), PWN.
Learning outcomes:	Knowledge: 1. understands well the role and meaning of the computer metaphor both as an empirical hypothesis in cognitive science and analogy in the preparation of artificial intelligence programs. 2. has a basic knowledge of the main types of mental representations and mental procedures postulated in cognitive science. 3. knows the basics of the empirical methodology of cognitive science. 4. knows the assumptions and goals of modeling cognitive behavior of people using the methods of artificial intelligence. 5. knows the psychological roots of tools and concepts used in artificial intelligence and in the representation of knowledge. 6. knows the cognitive roots of the human-computer interaction field. Skills: 1. can explain selected human behaviors in terms of information processing (K_U11). 2. analyzes individual methods of artificial intelligence due to its suitability for modeling particular cognitive behaviors of a human being (K_U12). 3. has in-depth communication skills with experts in the fields of cognitive science, cognitive psychology and cognitive neuroscience who do not have IT knowledge (K_U11). 4. can describe the tools of artificial intelligence and computational models of human behavior (K_U12). Competence: 1. has elementary preparation for cooperation in teams conducting cognitive research (K_K02). 2. knows the limits of his/her own knowledge and understands the need for further education, including the acquisition of knowledge from outside the field of computer science (K_K01). 3. is able to precisely formulate questions that serve to deepen one's understanding of a given topic, in particular when dealing with experts in cognitive science, cognitive psychology and cognitive neuroscience (K_K02). 4. can present IT issues related to cognitive science (K_K06).
Assessment methods and assessment criteria:	- class attendance (at seminars) - elaboration of selected articles, preparation of its presentations and giving talks in the seminar - written exam on the knowledge of the issues presented during lectures

This course is not currently offered.

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Copyright by University of Warsaw.