200077 SE Vertiefungsseminar: Geist und Gehirn (2020S)
Bayesian Statistics and Hierarchical Bayesian Modeling for Psychological Science
Prüfungsimmanente Lehrveranstaltung
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.
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Mo 03.02.2020 11:00 bis Di 25.02.2020 11:00
- Abmeldung bis Fr 06.03.2020 11:00
Details
max. 20 Teilnehmer*innen
Sprache: Englisch
Lehrende
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.
To install R: https://www.r-project.org/
To install RStudio: https://www.rstudio.com/Example slides: https://git.io/JvOtk
- Mittwoch 11.03. 09:45 - 11:15 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
- Mittwoch 18.03. 09:45 - 11:15 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
- Freitag 27.03. 13:15 - 14:45 Hörsaal F Psychologie, Liebiggasse 5 1. Stock
- Mittwoch 01.04. 09:45 - 11:15 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
- Mittwoch 22.04. 09:45 - 11:15 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
- Mittwoch 29.04. 09:45 - 11:15 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
- Mittwoch 06.05. 09:45 - 11:15 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
- Mittwoch 13.05. 09:45 - 11:15 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
- Mittwoch 20.05. 09:45 - 11:15 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
- Mittwoch 27.05. 09:45 - 11:15 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
- Mittwoch 03.06. 09:45 - 11:15 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
- Mittwoch 10.06. 09:45 - 11:15 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
- Mittwoch 17.06. 09:45 - 11:15 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
- Mittwoch 24.06. 09:45 - 11:15 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
Art der Leistungskontrolle und erlaubte Hilfsmittel
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%)
Mindestanforderungen und Beurteilungsmaßstab
[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%
Prüfungsstoff
- 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
Literatur
[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.
Zuordnung im Vorlesungsverzeichnis
Letzte Änderung: Do 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.