Introduction to Cognitive Science

General data

Course ID:	3501-KOG-WK
Erasmus code / ISCED:	08.101 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. / (0223) Philosophy and ethics The ISCED (International Standard Classification of Education) code has been designed by UNESCO.
Course title:	Introduction to Cognitive Science
Name in Polish:	Wstęp do kognitywistyki
Organizational unit:	Institute of Philosophy
Course groups:
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:	Polish
Prerequisites (description):	(in Polish) Zajęcia o charakterze podstawowym.
Mode:	Classroom
Short description:	The students learn the interdisciplinary project of cognitive science and its basic paradigms. Basic research problems along with proposed solutions are introduced. The topics covered include classical cognitivism, computational modelling, connectionism, computer metaphor, artificial intelligence, emergentism, dynamical systems, Bayesian accounts of cognition, logical approaches to the study of reasoning, embodied cognition, the notion of representation and its criticisms, as well as models of consciousness and the notion of information.
Full description:	The students learn the interdisciplinary project of cognitive science and its basic paradigms. Basic research problems along with proposed solutions are introduced. The topics covered include classical cognitivism, computational modelling, connectionism, computer metaphor, artificial intelligence, emergentism, dynamical systems, Bayesian accounts of cognition, logical approaches to the study of reasoning, embodied cognition, the notion of representation and its criticisms, as well as models of consciousness and the notion of information. The lecture will be based primarily on case studies. We focus on cases that inspired many later, similar models. We will start with the classical computational / symbolic theory of cognition, and then look at connectionist models. Then we will deal with the recent ideas: dynamic systems, Bayesian models, sensorimotor accounts and embodied cognition, and behavioural robotics. We will also look at the role of mental representation in explanations of cognition in various approaches to simulation and modelling. Some even deny that the mind represents at all. What does this mean? The lecture is an introduction to the methodology of cognitive science. The introduction highlights the explanatory pluralism of the contemporary (and earlier) research. Estimated number of hours a student should spend on achieving learning outcomes: 30h (lecture) + 45h self-study Lecture topics 1. The nature of explanation in cognitive science. What is explanation? Competence and performance, functional and mechanistic explanation 2. Simulation, computation and modelling: Chinese room 3. Symbolic computation. Newell and Simon’s GPS as a model of cognition 4. Computational neuroscience. Marr’s theory of theory and three levels of explanation 5. Computational neuroscience. A connectionist model of learning of past tense for English verbs: Rumelhart and McClelland 6. Dynamic systems in explaining the developmental processes in children (Thelen and Smith) 7. Probabilistic (Bayesian) models of human rationality (Oaksford and Chater) 8. Logic and thinking: Wason's task in the light of nonmonotonic logic 9. Behavioral or cognitive robotics? Phonotaxia in robotic crickets (B. Webb) 10. Explanatory role of representation. The classic approach 11. Explanatory role of representation. Imagery debate. 12. Explanatory role of representation. Connectionism 13. Explanatory role of representation. Behavioural robotics. 14. The concept of information and representation 15. Modelling in cognitive science. Consciousness. Explanatory pluralism
Bibliography:	(in Polish) JOHNSON-LAIRD, P., 1999, Komputer a umysł. Wstęp do nauk poznawczych, przeł. P. Jaśkowski, Protext, Poznań. HOHOL, M., 2013, Wyjaśnić umysł, Copernicus Center Press, Kraków. PINKER, S., 2002, Jak działa umysł, przeł. M. Koraszewska, KiW, Warszawa. URCHS, M., 2009, O procesorach i procesach myślowych. Elementy kognitywistyki, Wydawnictwo UMK, Toruń. CLARK, A., 2001, Mindware: An Introduction to the Philosophy of Cognitive Science, Oxford University Press, Oxford. Teksty źródłowe dostępne w języku polskim: • A. Newell, H. Simon, GPS – Program, który symuluje myśl ludzką, przeł. D. Gajkowicz, w: Maszyny matematyczne a myślenie, E. Feigenbaum i J. Feldman, PWN, Warszawa, s. 275-290. • John Searle, Umysły, mózgi i programy, w: B. Chwedeńczuk (red.), Filozofia umysłu, Warszawa 1995 • Zenon W. Pylyshyn, Spór o wyobraźnię: medium analogowe czy wiedza ukryta? W „Psychologia poznawcza w trzech ostatnich dekadach XX wieku”, red. Zbigniew Chlewiński, tłum. Jacek Suchecki: 366–408. Gdańsk 2007.
Learning outcomes:	(in Polish) nabyta wiedza - student zna podstawowe pojęcia kognitywistyki [K_W01, K_W04] - student zna podstawowe pojęcia matematyczne stosowane w opisie procesów poznawczych, takie jak maszyna Turinga, sieć neuropodobna, układ dynamiczny [K_W03, K_W05, K_W07, K_W09, K_W11] - student zna podstawowe zasady metodologiczne w poszczególnych nurtach badań [K_W04, K_W05, K_W06] - student zna podstawowe teorie reprezentacji [K_W04, K_W05, K_W08] nabyte umiejętności - student potrafi wyróżniać podstawowe nurty badawcze w kognitywistyce [K_W01, K_U01] - potrafi wskazywać wyjaśniane zjawiska i składniki wyjaśnień [K_W02, K_U10, K_U19, K_U22] - potrafi wskazywać role, jaką pełnią w wyjaśnieniach reprezentacje, modele obliczeń [K_U03, K_U08] - potrafi analizować argumenty i kontrargumenty za różnymi strategiami modelowania zjawisk poznawczych [K_U05, ] nabyte kompetencje społeczne - umie uważnie słuchać innych [K_K08] - potrafi zadawać w odpowiednim czasie pytania wykładowcy, które ułatwiają zrozumienie materiału jemu samemu oraz innym studentom [K_K02]
Assessment methods and assessment criteria:	(in Polish) Zob. opis zajęć w danym roku akademickim.

This course is not currently offered.

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