Bayesian models of cognition WF-R-PS-WMBM
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.
(in Polish) Grupa przedmiotów ogólnouczenianych
Learning outcome code/codes
Type of subject
Course coordinators
Learning outcomes
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 criteria
Final exam concerning the main issues discussed during the lecture.
A student should receive at least 50% score to pass the exam.
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
Additional information
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