200149 SE Theorie und Empirie wissenschaftlichen Arbeitens (Geist und Gehirn) 1 (2020W)
Advanced Data Analysis
Prüfungsimmanente Lehrveranstaltung
Labels
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Di 01.09.2020 07:00 bis Do 24.09.2020 07:00
- Abmeldung bis Fr 02.10.2020 07:00
Details
max. 20 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
[!IMPORTANT!] The first lecture will be on 06.10 (HS A, NIG), if possible. There will be NO lecture on 07.10.
[!IMPORTANT!] If possible, we will meet in person in the seminar room for the very first session (will be communicated via email). All other 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 TEWA is an extension of the 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 the course.[Language] This course will be taught in English.[Prerequisite] Basic knowledge of R is a plus (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.- Dienstag 06.10. 11:30 - 14:45 Hörsaal A Psychologie, NIG 6.Stock A0606
- Mittwoch 07.10. 15:00 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
- Mittwoch 07.10. 16:45 - 18:15 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
- Mittwoch 14.10. 15:00 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
- Mittwoch 14.10. 16:45 - 18:15 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
- Mittwoch 21.10. 15:00 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
- Mittwoch 21.10. 16:45 - 18:15 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
- Mittwoch 28.10. 15:00 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
- Mittwoch 28.10. 16:45 - 18:15 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
- Mittwoch 04.11. 15:00 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
- Mittwoch 04.11. 16:45 - 18:15 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
- Mittwoch 11.11. 15:00 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
- Mittwoch 11.11. 16:45 - 18:15 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
- Mittwoch 18.11. 15:00 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
- Mittwoch 18.11. 16:45 - 18:15 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
- Mittwoch 25.11. 15:00 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
- Mittwoch 25.11. 16:45 - 18:15 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
- Mittwoch 02.12. 15:00 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
- Mittwoch 02.12. 16:45 - 18:15 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
- Mittwoch 09.12. 15:00 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
- Mittwoch 09.12. 16:45 - 18:15 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
- Mittwoch 16.12. 15:00 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
- Mittwoch 16.12. 16:45 - 18:15 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
- Mittwoch 13.01. 15:00 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
- Mittwoch 13.01. 16:45 - 18:15 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
- Mittwoch 20.01. 15:00 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
- Mittwoch 20.01. 16:45 - 18:15 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
- Mittwoch 27.01. 15:00 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
- Mittwoch 27.01. 16:45 - 18:15 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
Art der Leistungskontrolle und erlaubte Hilfsmittel
[Assessment]
Regular participation (25%)
Regular programming tutorials (likely via datacamp.com) (35%)
Programming project (25%)
Exam (15%)
Regular participation (25%)
Regular programming tutorials (likely via datacamp.com) (35%)
Programming project (25%)
Exam (15%)
Mindestanforderungen und Beurteilungsmaßstab
[Minimum requirements]
- Basic knowledge about statistics (e.g., t-test, ANOVA, correlation, regression)
- Basic R skills (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 (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 apply regression models appropriately
- 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 apply regression models appropriately
- 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. 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]
- 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.
- 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]
- 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: Di 16.02.2021 14:29
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 both theoretical and practical parts.During the current situation, practical sessions will take place in two ways. (a) self-pace learning on a online learning platform (e.g., datacamp.com); (b) small group (<=5 people) tutorial sessions with the lecturer.Throughout the course, students are asked to (a) actively participate in discussions, (b) complete designated tutorial sessions on the selected online learning platform (e.g., datacamp.com).[METHODS]
Oral presentations by lecturer, in-class (online) participation, paper review, regular programming tutorials, brief demonstration of running Stan on High Performance Computing (HPC) Clusters.