200075 SE Vertiefungsseminar: Geist und Gehirn (2019S)
Bayesian Statistics and Hierarchical Bayesian Modeling for Psychological Science
Prüfungsimmanente Lehrveranstaltung
Labels
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 04.02.2019 09:00 bis Di 26.02.2019 09:00
- Abmeldung bis Mo 04.03.2019 09:00
Details
max. 20 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
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/
Dienstag
05.03.
13:15 - 14:45
Hörsaal F Psychologie, Liebiggasse 5 1. Stock
Dienstag
19.03.
13:15 - 14:45
Hörsaal F Psychologie, Liebiggasse 5 1. Stock
Dienstag
26.03.
13:15 - 14:45
Hörsaal F Psychologie, Liebiggasse 5 1. Stock
Dienstag
02.04.
13:15 - 14:45
Hörsaal F Psychologie, Liebiggasse 5 1. Stock
Dienstag
09.04.
13:15 - 14:45
Hörsaal F Psychologie, Liebiggasse 5 1. Stock
Dienstag
30.04.
13:15 - 14:45
Hörsaal F Psychologie, Liebiggasse 5 1. Stock
Dienstag
07.05.
13:15 - 14:45
Hörsaal F Psychologie, Liebiggasse 5 1. Stock
Dienstag
14.05.
13:15 - 14:45
Hörsaal F Psychologie, Liebiggasse 5 1. Stock
Dienstag
21.05.
13:15 - 14:45
Hörsaal F Psychologie, Liebiggasse 5 1. Stock
Dienstag
04.06.
11:30 - 13:00
Hörsaal H Psychologie KG Liebiggasse 5
Dienstag
04.06.
13:15 - 14:45
Hörsaal F Psychologie, Liebiggasse 5 1. Stock
Dienstag
18.06.
11:30 - 13:00
Hörsaal H Psychologie KG Liebiggasse 5
Dienstag
18.06.
13:15 - 14:45
Hörsaal F Psychologie, Liebiggasse 5 1. Stock
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
Art der Leistungskontrolle und erlaubte Hilfsmittel
Mindestanforderungen und Beurteilungsmaßstab
[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
Prüfungsstoff
Will be discussed in course.
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.
- 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.
Zuordnung im Vorlesungsverzeichnis
Letzte Änderung: 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.