Universität Wien FIND

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200140 SE Vertiefungsseminar: Geist und Gehirn (2019W)

Bayesian Statistics and Hierarchical Bayesian Modeling for Psychological Science

4.00 ECTS (2.00 SWS), SPL 20 - Psychologie
Prüfungsimmanente Lehrveranstaltung

Vertiefungsseminare können nur fürs Pflichtmodul B verwendet werden! Eine Verwendung fürs Modul A4 Freie Fächer ist nicht möglich.



max. 20 Teilnehmer*innen
Sprache: Englisch


Termine (iCal) - nächster Termin ist mit N markiert

This course will be taught in English!
*Basic knowledge of R* is a must (see below)!

Please bring your laptop to the class, because there will be practical sessions. In case you do not have one, please share with your neighbors.
Please install R and RStudio before the first lecture (09.10.2019).
To install R: https://www.r-project.org/
To install RStudio: https://www.rstudio.com/

Mittwoch 09.10. 11:30 - 13:00 Hörsaal II NIG Erdgeschoß
Mittwoch 16.10. 11:30 - 13:00 Hörsaal II NIG Erdgeschoß
Mittwoch 23.10. 11:30 - 13:00 Hörsaal II NIG Erdgeschoß
Mittwoch 30.10. 11:30 - 13:00 Hörsaal II NIG Erdgeschoß
Mittwoch 06.11. 11:30 - 13:00 Hörsaal II NIG Erdgeschoß
Mittwoch 13.11. 11:30 - 13:00 Hörsaal II NIG Erdgeschoß
Mittwoch 20.11. 11:30 - 13:00 Hörsaal II NIG Erdgeschoß
Mittwoch 27.11. 11:30 - 13:00 Hörsaal II NIG Erdgeschoß
Mittwoch 04.12. 11:30 - 13:00 Hörsaal II NIG Erdgeschoß
Mittwoch 11.12. 11:30 - 13:00 Hörsaal II NIG Erdgeschoß
Dienstag 07.01. 11:30 - 13:00 Hörsaal D Psychologie, NIG 6.Stock A0624
Mittwoch 08.01. 11:30 - 13:00 Hörsaal II NIG Erdgeschoß
Mittwoch 22.01. 11:30 - 13:00 Hörsaal II NIG Erdgeschoß
Mittwoch 29.01. 11:30 - 13:00 Hörsaal II NIG Erdgeschoß


Ziele, Inhalte und Methode der Lehrveranstaltung

Computational modeling and mathematical modeling provide an insightful quantitative framework that allows researchers to inspect latent processes and to understand hidden mechanisms. Hence, cognitive modeling has gained increasing attention in many areas of cognitive science and neuroscience. One illustration of this trend is the growing popularity of Bayesian approaches to cognitive modeling.

To this aim, this course teaches the theoretical and practical knowledge necessary to perform, evaluate and interpret Bayesian modeling analyses. Target group is students that plan or already started a master's or doctoral thesis using computational modeling.

This course is dedicated to introducing students to the basic knowledge of Bayesian statistics as well as basic techniques of Bayesian cognitive modeling. We will use R/RStudio and a newly developed statistical computing language - Stan (mc-stan.org) to perform Bayesian analyses, ranging from simple binomial model and linear regression model to more complex hierarchical models. Time will be allocated for in-class exercise. A brief introduction to R is also provided at the beginning of the course.

Oral presentations by lecturer and students, in-class participation, homeworks, oral presentations of modeling projects, quizzes, brief demonstration of running Stan on High Performance Computing (HPC) Clusters.

Art der Leistungskontrolle und erlaubte Hilfsmittel

Regular participation (30%)
Review of paper #1 (25%)
Review of paper #2 (25%)
Programming work (20%)

>87% 1; >75% 2; >63% 3; >51% 4; <=50% 5

Mindestanforderungen und Beurteilungsmaßstab

- Basic knowledge about statistics (e.g., t-test, regression)
- Basic R skills, MUST! (e.g., indexing, if-else statement, for loop)


- Able to provide a basic understanding of Bayesian statistics
- Able to understand the difference between Bayesian inference and frequentist inference
- Able to describe rephrase the concept of cognitive modeling and judge when to use it
- Able to write a simple cognitive model (e.g., Rescorla-Wagner model) in the Stan language


[Journal articles]
- Kruschke, J. K., & Liddell, T. M. (2018). Bayesian data analysis for newcomers. Psychonomic bulletin & review, 25(1), 155-177.
- Wagenmakers, E. J., Marsman, M., Jamil, T., Ly, A., Verhagen, J., Love, J., ... & Matzke, D. (2018). Bayesian inference for psychology. Part I: Theoretical advantages and practical ramifications. Psychonomic bulletin & review, 25(1), 35-57.
- Wilson, R., & Collins, A. (2019). Ten simple rules for the computational modeling of behavioral data. psyarxiv,
- Daw, N. D. (2011). Trial-by-trial data analysis using computational models. Decision making, affect, and learning: Attention and performance XXIII, 23, 3-38.

- McElreath, R. (2016). Statistical Rethinking: A Bayesian Course with Examples in R and Stan. CRC Press.
- Lambert, B. (2018). A Student’s Guide to Bayesian Statistics. Sage.

[Extended reading]
- Ahn, W. Y., Haines, N., & Zhang, L. (2017). Revealing neurocomputational mechanisms of reinforcement learning and decision-making with the hBayesDM package. Computational Psychiatry, 1, 24-57.
- Botvinik-Nezer, R., Holzmeister, F., Camerer, C. F., Dreber, A., Huber, J., Johannesson, M., ... & Avesani, P. (2020). Variability in the analysis of a single neuroimaging dataset by many teams. Nature, 1-7.
- Zhang, L., Lengersdorff, L., Mikus, N., Gläscher, J., & Lamm, C. (2020). Using reinforcement learning models in social neuroscience: frameworks, pitfalls and suggestions of best practices. Social cognitive and affective neuroscience, 15(6), 695-707.
- Hu, Y., He, L., Zhang, L., Wölk, T., Dreher, J. C., & Weber, B. (2018). Spreading inequality: neural computations underlying paying-it-forward reciprocity. Social cognitive and affective neuroscience, 13(6), 578-589.
- Zhang, L., & Gläscher, J. (2020). A brain network supporting social influences in human decision-making. Science advances, 6(34), eabb4159.
- Crawley, D., Zhang, L., Jones, E. J., Ahmad, J., Caceres, A. S. J., Oakley, B., ... & den Ouden, H. (2019). Modeling cognitive flexibility in autism spectrum disorder and typical development reveals comparable developmental shifts in learning mechanisms.
- Zhang, L., Redžepović, S., Rose, M., & Gläscher, J. (2018). Zen and the Art of Making a Bayesian Espresso. Neuron, 98(6), 1066-1068.

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Mo 19.10.2020 11:29