Bayesian models of cognition

General data

Course ID:	WF-R-PS-WMBM
Erasmus code / ISCED:	14.4 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. / (unknown)
Course title:	Bayesian models of cognition
Name in Polish:	WM: Bayesowskie modele poznawcze
Organizational unit:	Institute of Psychology
Course groups:	(in Polish) Grupa przedmiotów ogólnouczelnianych - Doktoranci (in Polish) Przedmioty dla doktorantów psychologii (in Polish) Wykłady monograficzne kierunkowe z psychologii (in Polish) Wykłady monograficzne pozakierunkowe
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
Learning outcome code/codes:	SD_ PS _W02 SD_ PS _W04 SD_ PS _W05 SD_ PS _K04 SD_ PS _K05
Short description:	Human or animal cognitive systems have to deal with uncertainty implied by complex and floating data coming from the environment. It seems that our cognitive system deals with this challenge surprisingly well. During two last decades cognitive psychology has tried to explain why and due to which strategies such success is accomplished. Probabilistic models, known as Bayesian approach, describe such processes as perception, language processing or reasoning in terms of probabilistic reasoning. The aim of the lecture is to introduce the basic assumptions of the probabilistic approach in cognitive psychology.
Full description:	Topics: 1. Introduction. Bayesian aproach to cognition. Levels of explanation. 2. Probabilistic inference and rational analysis. 3. Perception as probabilistic inference. 4. Reasoning from Bayesian perspective. 5. Models of judgment and decision making. Pragmatic conditions of rationality assessment. 6. Rational model of information aquisition. 7. Bayesian models of memory. 8. Pseudocontingency. Learning of causality. Summary.
Bibliography:	Knill, D., & Richards, W. (Eds.). (1996). Perception as Bayesian Inference. Cambridge: Cambridge University Press. Chater, N., & Oaksford, M. (Eds.). (2008). The Probabilistic Mind: Prospects for Bayesian cognitive science. Oxford: Oxford University Press. Oaksford, M., Chater N. (2007). Bayesian Rationality. Oxford University Press, New York
Efekty kształcenia i opis ECTS:	Knowledge: A student knows and understands origins and aims of probabilistic approach in cognitive psychology as well as its placement in the history of psychological ideas. A student knows and understands the specificity of bayesian approach to cognition and he/she is able to explain what is the rational analysis and what are its limitations. ECTS: 1 point 15 hr classes attendance 10 hr readings 5 hr exam preparation
Assessment methods and assessment criteria:	Final exam concerning the main issues discussed during the lecture. A student should receive at least 50% score to pass the exam.

This course is not currently offered.

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