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200075 SE Advanced Seminar: Mind and Brain (2019S)
Bayesian Statistics and Hierarchical Bayesian Modeling for Psychological Science
Continuous assessment of course work
Labels
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 04.02.2019 09:00 to Tu 26.02.2019 09:00
- Deregistration possible until Mo 04.03.2019 09:00
Details
max. 20 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
This course will be taught in English!
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 (05.03.2019).
To install R: https://www.r-project.org/
To install RStudio: https://www.rstudio.com/
- Tuesday 05.03. 13:15 - 14:45 Hörsaal F Psychologie, Liebiggasse 5 1. Stock
- Tuesday 19.03. 13:15 - 14:45 Hörsaal F Psychologie, Liebiggasse 5 1. Stock
- Tuesday 26.03. 13:15 - 14:45 Hörsaal F Psychologie, Liebiggasse 5 1. Stock
- Tuesday 02.04. 13:15 - 14:45 Hörsaal F Psychologie, Liebiggasse 5 1. Stock
- Tuesday 09.04. 13:15 - 14:45 Hörsaal F Psychologie, Liebiggasse 5 1. Stock
- Tuesday 30.04. 13:15 - 14:45 Hörsaal F Psychologie, Liebiggasse 5 1. Stock
- Tuesday 07.05. 13:15 - 14:45 Hörsaal F Psychologie, Liebiggasse 5 1. Stock
- Tuesday 14.05. 13:15 - 14:45 Hörsaal F Psychologie, Liebiggasse 5 1. Stock
- Tuesday 21.05. 13:15 - 14:45 Hörsaal F Psychologie, Liebiggasse 5 1. Stock
- Tuesday 04.06. 11:30 - 13:00 Hörsaal H Psychologie KG Liebiggasse 5
- Tuesday 04.06. 13:15 - 14:45 Hörsaal F Psychologie, Liebiggasse 5 1. Stock
- Tuesday 18.06. 11:30 - 13:00 Hörsaal H Psychologie KG Liebiggasse 5
- Tuesday 18.06. 13:15 - 14:45 Hörsaal F Psychologie, Liebiggasse 5 1. Stock
Information
Aims, contents and method of the course
Assessment and permitted materials
Minimum requirements and assessment criteria
[Minimum requirements]
- Basic knowledge about statistics (e.g., t-test, regression)
- Basic R skills (but not a must)[Assessment criteria]
- Able to provide a basic understanding of Bayesian statistics
- Able to understand the difference between Bayesian inference and frequentist inference
- Able to describe 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
- Basic knowledge about statistics (e.g., t-test, regression)
- Basic R skills (but not a must)[Assessment criteria]
- Able to provide a basic understanding of Bayesian statistics
- Able to understand the difference between Bayesian inference and frequentist inference
- Able to describe 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
Examination topics
Will be discussed in course.
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.
- Daw, N. D. (2011). Trial-by-trial data analysis using computational models. Decision making, affect, and learning: Attention and performance XXIII, 23, 3-38.
- Etz, A., Gronau, Q. F., Dablander, F., Edelsbrunner, P. A., & Baribault, B. (2018). How to become a Bayesian in eight easy steps: An annotated reading list. Psychonomic Bulletin & Review, 25(1), 219-234.
- 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.[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.
- Daw, N. D. (2011). Trial-by-trial data analysis using computational models. Decision making, affect, and learning: Attention and performance XXIII, 23, 3-38.
- Etz, A., Gronau, Q. F., Dablander, F., Edelsbrunner, P. A., & Baribault, B. (2018). How to become a Bayesian in eight easy steps: An annotated reading list. Psychonomic Bulletin & Review, 25(1), 219-234.
- 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.[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: Fr 15.01.2021 00:19
Computational modeling and mathematical modeling provide an insightful quantitative framework that allows researchers to inspect latent processes and to understand hidden mechanisms. Hence, computational modeling has gained increasing attention in many areas of cognitive science and neuroscience (hence, cognitive modeling). 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 exercises. A brief introduction to R is also provided at the beginning of the course.[METHODS]
Oral presentations by lecturer and students, in-class participation, homework, oral presentations of modeling projects, quizzes, a brief demonstration of running Stan on High Performance Computing (HPC) Clusters.