200077 SE Vertiefungsseminar: Geist und Gehirn (2021S)
Bayesian Statistics and Hierarchical Bayesian Modeling for Psychological Science
Prüfungsimmanente Lehrveranstaltung
Labels
DIGITAL
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 Di 02.02.2021 09:00 bis Mi 24.02.2021 09:00
- Abmeldung bis Mi 03.03.2021 09:00
Details
max. 20 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
[covid-19] All lectures will take place largely online: we meet virtually at the respective times using a video conference tool (will be communicated via email). These sessions will be live and will be recorded for future revisits if nobody objects.
[Good-to-Know] This course is the well-received award-winning* course BayesCog (https://github.com/lei-zhang/BayesCog_Wien). Please check out the course contents to see if you are interested, before signing up for the course.[Language] This course will be taught in English.[Prerequisite] Basic knowledge of R is a MUST (see below)! If you indeed know nothing about R, you are more than encouraged to check out this article: http://shorturl.at/qrHIP*This course was awarded a SIPS commendation (Society for the Improvement of Psychological Science) in 2020, as well as an Early Career Researcher Teaching Award from the University of Vienna.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
Freitag
19.03.
13:15 - 14:45
Digital
Freitag
26.03.
13:15 - 14:45
Digital
Freitag
16.04.
13:15 - 14:45
Digital
Freitag
23.04.
13:15 - 14:45
Digital
Freitag
30.04.
13:15 - 14:45
Digital
Freitag
07.05.
13:15 - 14:45
Digital
Freitag
14.05.
13:15 - 14:45
Digital
Freitag
21.05.
13:15 - 14:45
Digital
Freitag
28.05.
13:15 - 14:45
Digital
Freitag
04.06.
13:15 - 14:45
Digital
Freitag
11.06.
13:15 - 14:45
Digital
Freitag
18.06.
13:15 - 14:45
Digital
Freitag
25.06.
13:15 - 14:45
Digital
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
Art der Leistungskontrolle und erlaubte Hilfsmittel
Regular participation (25%)
Review of paper (25%)
Regular programming tutorials (via datacamp.com) (25%)
Programming project (25%)
Review of paper (25%)
Regular programming tutorials (via datacamp.com) (25%)
Programming project (25%)
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
[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 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
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.
- van de Schoot, R., Depaoli, S., King, R., Kramer, B., Märtens, K., Tadesse, M. G., ... & Yau, C. (2021). Bayesian statistics and modelling. Nature Reviews Methods Primers, 1(1), 1-26.
- Wilson, R. C., & Collins, A. G. (2019). Ten simple rules for the computational modeling of behavioral data. Elife, 8, e49547.
- Blohm, G., Kording, K. P., & Schrater, P. R. (2020). A how-to-model guide for Neuroscience. Eneuro, 7(1).
- 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.[Books]
- McElreath, R. (2020). Statistical rethinking: A Bayesian course with examples in R and Stan. 2nd Ed. CRC press.
- Lambert, B. (2018). A Student’s Guide to Bayesian Statistics. Sage.
- Gelman, A., Hill, J., & Vehtari, A. (2020). Regression and other stories. Cambridge University Press.[Extended reading]
- 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.
- 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.
- Zhao, Y., Rütgen, M., Zhang, L., & Lamm, C. (2021). Pharmacological fMRI provides evidence for opioidergic modulation of discrimination of facial pain expressions. Psychophysiology, 58(2), e13717.
- 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.
- van de Schoot, R., Depaoli, S., King, R., Kramer, B., Märtens, K., Tadesse, M. G., ... & Yau, C. (2021). Bayesian statistics and modelling. Nature Reviews Methods Primers, 1(1), 1-26.
- Wilson, R. C., & Collins, A. G. (2019). Ten simple rules for the computational modeling of behavioral data. Elife, 8, e49547.
- Blohm, G., Kording, K. P., & Schrater, P. R. (2020). A how-to-model guide for Neuroscience. Eneuro, 7(1).
- 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.[Books]
- McElreath, R. (2020). Statistical rethinking: A Bayesian course with examples in R and Stan. 2nd Ed. CRC press.
- Lambert, B. (2018). A Student’s Guide to Bayesian Statistics. Sage.
- Gelman, A., Hill, J., & Vehtari, A. (2020). Regression and other stories. Cambridge University Press.[Extended reading]
- 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.
- 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.
- Zhao, Y., Rütgen, M., Zhang, L., & Lamm, C. (2021). Pharmacological fMRI provides evidence for opioidergic modulation of discrimination of facial pain expressions. Psychophysiology, 58(2), e13717.
Zuordnung im Vorlesungsverzeichnis
Letzte Änderung: Fr 12.05.2023 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, 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.