200077 SE Advanced Seminar: Mind and Brain (2020S)
Bayesian Statistics and Hierarchical Bayesian Modeling for Psychological Science
Continuous assessment of course work
Labels
Dieses Vertiefungsseminar kann für alle Schwerpunkte absolviert werden!Vertiefungsseminare können nur für das Pflichtmodul B verwendet werden! Eine Verwendung für das Modul A4 Freie Fächer ist nicht möglich.
Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
- Registration is open from Mo 03.02.2020 11:00 to Tu 25.02.2020 11:00
- Deregistration possible until Fr 06.03.2020 11:00
Details
max. 20 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
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.
To install R: https://www.r-project.org/
To install RStudio: https://www.rstudio.com/Example slides: https://git.io/JvOtk
- Wednesday 11.03. 09:45 - 11:15 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
- Wednesday 18.03. 09:45 - 11:15 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
- Friday 27.03. 13:15 - 14:45 Hörsaal F Psychologie, Liebiggasse 5 1. Stock
- Wednesday 01.04. 09:45 - 11:15 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
- Wednesday 22.04. 09:45 - 11:15 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
- Wednesday 29.04. 09:45 - 11:15 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
- Wednesday 06.05. 09:45 - 11:15 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
- Wednesday 13.05. 09:45 - 11:15 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
- Wednesday 20.05. 09:45 - 11:15 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
- Wednesday 27.05. 09:45 - 11:15 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
- Wednesday 03.06. 09:45 - 11:15 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
- Wednesday 10.06. 09:45 - 11:15 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
- Wednesday 17.06. 09:45 - 11:15 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
- Wednesday 24.06. 09:45 - 11:15 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
Information
Aims, contents and method of the course
Assessment and permitted materials
Regular participation (30%)
Review of paper #1 (25%)
Review of paper #2 (25%)
Programming work (20%)
Review of paper #1 (25%)
Review of paper #2 (25%)
Programming work (20%)
Minimum requirements and assessment criteria
[Minimum requirements]
- Basic knowledge about statistics (e.g., t-test, ANOVA, correlation, regression)
- Basic R skills, MUST! (e.g., indexing, if-else statement, for loop)[Assessment criteria]
1: >87%
2: 76 - 87%
3: 64 - 75%
4: 51 - 63%
5: <=50%
- Basic knowledge about statistics (e.g., t-test, ANOVA, correlation, regression)
- Basic R skills, MUST! (e.g., indexing, if-else statement, for loop)[Assessment criteria]
1: >87%
2: 76 - 87%
3: 64 - 75%
4: 51 - 63%
5: <=50%
Examination topics
- 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
- 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
Reading list
[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.[Books]
- 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.
- 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., Oakley, B., San José Cáceres, A., ... & EU-AIMS LEAP group. (2020). Modeling flexible behavior in childhood to adulthood shows age-dependent learning mechanisms and less optimal learning in autism in each age group. PLoS biology, 18(10), e3000908.
- Zhang, L., Redžepović, S., Rose, M., & Gläscher, J. (2018). Zen and the Art of Making a Bayesian Espresso. Neuron, 98(6), 1066-1068.
- Bayer, J., Rusch, T., Zhang, L., Gläscher, J., & Sommer, T. (2020). Dose-dependent effects of estrogen on prediction error related neural activity in the nucleus accumbens of healthy young women. Psychopharmacology, 237(3), 745-755.
- Kreis, I., Zhang, L., Moritz, S., & Pfuhl, G. (2020). Spared performance but increased uncertainty in schizophrenia: evidence from a probabilistic decision-making task.
- Schmalz, X., Manresa, J. B., & Zhang, L. (2020). What is a Bayes Factor?.
- Kreis, I., Zhang, L., Mittner, M., Syla, L., Lamm, C., & Pfuhl, G. (2020). Aberrant uncertainty processing is linked to psychotic-like experiences, autistic traits and reflected in pupil dilation.
- 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.[Books]
- 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.
- 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., Oakley, B., San José Cáceres, A., ... & EU-AIMS LEAP group. (2020). Modeling flexible behavior in childhood to adulthood shows age-dependent learning mechanisms and less optimal learning in autism in each age group. PLoS biology, 18(10), e3000908.
- Zhang, L., Redžepović, S., Rose, M., & Gläscher, J. (2018). Zen and the Art of Making a Bayesian Espresso. Neuron, 98(6), 1066-1068.
- Bayer, J., Rusch, T., Zhang, L., Gläscher, J., & Sommer, T. (2020). Dose-dependent effects of estrogen on prediction error related neural activity in the nucleus accumbens of healthy young women. Psychopharmacology, 237(3), 745-755.
- Kreis, I., Zhang, L., Moritz, S., & Pfuhl, G. (2020). Spared performance but increased uncertainty in schizophrenia: evidence from a probabilistic decision-making task.
- Schmalz, X., Manresa, J. B., & Zhang, L. (2020). What is a Bayes Factor?.
- Kreis, I., Zhang, L., Mittner, M., Syla, L., Lamm, C., & Pfuhl, G. (2020). Aberrant uncertainty processing is linked to psychotic-like experiences, autistic traits and reflected in pupil dilation.
Association in the course directory
Last modified: Th 14.01.2021 18:28
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.[CONTENT]
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.[METHODS]
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.