Uniwersytet Warszawski - Centralny System UwierzytelnianiaNie jesteś zalogowany | zaloguj się
katalog przedmiotów - pomoc

Cognitive processes modelling I

Informacje ogólne

Kod przedmiotu: 2500-EN-COG-OB1Z-3 Kod Erasmus / ISCED: 14.4 / (0313) Psychologia
Nazwa przedmiotu: Cognitive processes modelling I
Jednostka: Wydział Psychologii
Grupy: Cognitive Science
Punkty ECTS i inne: 3.00
Język prowadzenia: angielski
Skrócony opis: (tylko po angielsku)

The course introduces students to the main approaches to the cognitive systems modeling, provides a broad overview of the modeling methods and their applications, with a focus on dynamical systems perspective. We also explore the need and possibilities of their integration for modeling complex cognitive phenomena.

Zajęcia w cyklu "Semestr zimowy 2019/20" (zakończony)

Okres: 2019-10-01 - 2020-01-27
Wybrany podział planu:


powiększ
zobacz plan zajęć
Typ zajęć: Wykład, 30 godzin więcej informacji
Koordynatorzy: (brak danych)
Prowadzący grup: Michał Denkiewicz, Joanna Rączaszek-Leonardi, Julian Zubek
Lista studentów: (nie masz dostępu)
Zaliczenie: Egzamin
Pełny opis: (tylko po angielsku)

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 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 populations exhibiting macro-dynamics. 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 hypotheses, plan further experiments, verify theories, and augment the overall understanding of cognitive processes. Last but not least, models of the cognitive processes are often inspirations for developing artificial cognitive systems, such as autonomous robots or software agents, which extend human agency in the world.

The aim of this course is to give an overview of the main paradigms, approaches and methods used to model processes of such 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.

Literatura: (tylko po angielsku)

Non-obligatory texts are italicized.

1. What is a cognitive system? A map for the course: the main paradigms in cognitive sciences and main paradigms of cognitive modeling.

a. Von Bertalanffy (1968) „General Systems Theory”: Intro 3-30 (In Polish: Ogólna Teoria Systemów: Wprowadzenie)

b. Sun, R. (2008). Introduction to Computational Cognitive Modeling. In R. Sun (Ed.). The Cambridge Handbook of Computational Psychology (pp. 3–19). Cambridge University Press.

c. In Polish: JRL: Dynamiczne i symboliczne oblicza kognitywistyki: sposoby integracji. Przegląd Filozoficzny – Nowa Seria, Nr 2 (86), s. 104-132, DOI: 10.2478/pfns-2013-0052.

2. Computational approaches to modeling complexity. Formal languages and automata. [MD]

a. http://www.scholarpedia.org/article/Turing_machine

b. Hopcroft, J. E., & Ullman, J. D. (2001). Introduction to Automata Theory, Languages and Computation. Adison-Wesley. Reading, Mass. Parts: 1, 8.1, 8.2, 8.6, 9, 10.1, 10.2

3. Dynamical systems I. Dynamical systems in modeling cognition: organization of cognitive systems (interaction dominance & synergies). [JRL]

a. Guastello, S.J. & Liebovitch, L.S. (2011 ) Introduction to non-linear dynamics and complexity. In: Chaos and Complexity in Psychology. CUP.

b. Van Orden, Holden and Turvey (2003). Self-organization of Cognitive Performance. JEP: General, Vol. 132, No. 3, 331–350

c. Rączaszek-Leonardi, J. (2016). Reconciled with complexity in research on cognitive systems. Avant, Vol. VII, No. 2, 117-138.

d. Richardson, M., Dale, R., & Marsh, K. (2014). Complex Dynamical Systems in Social and Personality Psychology. In H. Reis & C. Judd (Eds.), Handbook of Research Methods in Social and Personality Psychology. Cambridge: Cambridge Univ. Press

4. Describing systems dynamics I. Difference equations, differential equations, phase spaces, attractors, fractals. [JZ]

a. http://www.scholarpedia.org/article/Dynamical_systems

b. http://www.scholarpedia.org/article/Phase_space

c. http://www.scholarpedia.org/article/Attractor

5. Describing systems dynamics II. Fractal Dimensions, Recurrence Quantification Analysis. [JRL & JZ]

a. Fusaroli, R., Konvalinka, I., and Wallot, S. (2014). “Analyzing social interactions: the promises and challenges of using cross recurrence quantification analysis,” in Translational Recurrence. Springer Proceedings in Mathematics, eds N. Marwan, M. Riley, A. Giuliani, and C. L. Webber Jr. (London: Springer), 137–155. doi: 10.1007/978-3-319-09531-8_9

b. Wallot, S. & Leonardi, G. (2018): Analyzing Multivariate Dynamics Using Cross-Recurrence Quantification Analysis (CRQA), Diagonal-Cross-Recurrence Profiles (DCRP), and Multidimensional Recurrence Quantification Analysis (MdRQA) – A Tutorial in R. Frontiers in Psychology. https://www.frontiersin.org/articles/10.3389/fpsyg.2018.02232/full

c. G Holden, John. (2005). Gauging the fractal dimension of response times from cognitive tasks. Tutorials in contemporary nonlinear methods for the behavioral sciences.

6. Logic-based models of cognition. Inference using production rules. Belief–desire–intention model of reasoning. [JZ]

a. Bringsjord, S. (2008). Declarative/Logic-Based Cognitive Modeling. In R. Sun (Ed.), The Cambridge Handbook of Computational Psychology (Cambridge Handbooks in Psychology, pp. 127-169). Cambridge: Cambridge Univ. Press

7. Grammars and Formal Languages [MD]

a. https://plato.stanford.edu/entries/computational-linguistics/ : parts: 1, 2, 3, 4.1, 6, 10

b. Hopcroft, J. E., & Ullman, J. D. (2001). Introduction to Automata Theory, Languages and Computation. Adison-Wesley. Reading, Mass. Parts: 3.1, 3.2, 5.1, 5.2

8. Game theory from the perspective of cognitive systems [MD]

a. Jackson, M. O. (2011). A brief introduction to the basics of game theory. Available at SSRN 1968579.

b. Rubinstein, A. (1998) Modeling Bounded Rationality, MIT Press, chapters: 1, 8, 11

c. Bonanno, G. Game Theory. An open access textbook with 165 solved exercises, https://arxiv.org/pdf/1512.06808.pdf, chapters: 1, 2, 9, 13

9. Statistical models I. The concept of probability and its interpretation. Statistics as a cornerstone of model evaluation. Rational agents and uncertainty. Ecological rationality and bias-variance dilemma. [JZ]

a. Romeijn, J.W. (2014). Philosophy of Statistics, Stanford Encyclopedia of Philosophy: https://plato.stanford.edu/entries/statistics/#StaInd

b. Gigerenzer, G., & Brighton, H. (2009). Homo Heuristicus: Why Biased Minds Make Better Inferences. Topics in Cognitive Science, 1(1), 107–143.

c. Zubek, J., Denkiewicz, M., Dębska, A., Radkowska, A., Komorowska-Mach, J., Litwin, P., … Rączaszek-Leonardi, J. (2016). Performance of language-coordinated collective systems: A study of wine recognition and description. Frontiers in Psychology, 7, 1321.

10. Statistical models II. Modeling uncertainty with statistical models. Introduction to Bayesian modeling. [MD]

a. Leo Breiman et al. Statistical modeling: The two cultures (with comments and a rejoinder by the author). Statistical Science, 16 (3):199–231, 2001. (comments not obligatory, but recommended)

b. Lee, M. D., & Wagenmakers, E.-J. (2014). Bayesian Cognitive Modeling: A Practical Course. Cambridge ; New York: Cambridge University Press.

11. Statistical models III. Bayesian methods in modeling action and perception. Free Energy Minimization. [MD]

a. Karl Friston, Christopher Thornton, and Andy Clark. Free-energy minimization and the dark-room problem. Frontiers in Psychology, 3:130, 2012.

b. Mathys C, Jean Daunizeau J, Karl J Friston KJ, Klaas Enno Stephan KE. (2011) A Bayesian foundation for individual learning under uncertainty Front. Hum. Neurosci. 5:35

c. Karl J Friston, Jean Daunizeau, and Stefan J Kiebel. Reinforcement learning or active inference? PloS one, 4(7): e6421, 2009.

12. Nature-inspired computation I: Neural networks as universal models of nonlinear dynamics. Feedforward and recurrent architectures. [JZ]

a. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning, MIT Press. (selected parts)

b. Thomas, M., & McClelland, J. (2008). Connectionist Models of Cognition. In R. Sun (Ed.), The Cambridge Handbook of Computational Psychology (Cambridge Handbooks in Psychology, pp. 23-58). Cambridge: Cambridge University Press.

13. Nature-inspired computation II: Evolutionary computation and evolutionary robotics. [MD]

a. Doncieux, S., Bredeche, N., Mouret, J. B., & Eiben, A. E. G. (2015). Evolutionary robotics: what, why, and where to. Frontiers in Robotics and AI, 2, 4.

b. http://www.scholarpedia.org/article/Genetic_algorithms

14. Evolution of artificial intelligence: a case study. [JZ]

15. The battle of models. Integrating approaches: hybrid models and beyond

a. McClelland, J. L. (2009). The place of modeling in cognitive science. Topics in Cognitive Science, 1 , 11–38.

b. Rick Dale (2008) The possibility of a pluralist cognitive science, Journal of Experimental & Theoretical Artificial Intelligence, 20:3, 155-179, DOI: 10.1080/09528130802319078

Opisy przedmiotów w USOS i USOSweb są chronione prawem autorskim.
Właścicielem praw autorskich jest Uniwersytet Warszawski.