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200005 PS Introductory Seminar Biological Basis of Experience and Behaviour (2020S)

Decision Neuroscience

6.00 ECTS (2.00 SWS), SPL 20 - Psychologie
Continuous assessment of course work

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).

Details

max. 40 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

This course will be taught in English!

Example slides: https://bit.ly/2U48Mkh

Wednesday 11.03. 16:45 - 18:15 Hörsaal D Psychologie, NIG 6.Stock A0624
Wednesday 18.03. 16:45 - 18:15 Hörsaal D Psychologie, NIG 6.Stock A0624
Friday 27.03. 16:45 - 18:15 Hörsaal C Psychologie, NIG 6.Stock A0618
Wednesday 01.04. 16:45 - 18:15 Hörsaal D Psychologie, NIG 6.Stock A0624
Wednesday 22.04. 16:45 - 18:15 Hörsaal D Psychologie, NIG 6.Stock A0624
Wednesday 29.04. 16:45 - 18:15 Hörsaal D Psychologie, NIG 6.Stock A0624
Wednesday 06.05. 16:45 - 18:15 Hörsaal D Psychologie, NIG 6.Stock A0624
Wednesday 13.05. 16:45 - 18:15 Hörsaal D Psychologie, NIG 6.Stock A0624
Wednesday 20.05. 16:45 - 18:15 Hörsaal D Psychologie, NIG 6.Stock A0624
Wednesday 27.05. 16:45 - 18:15 Hörsaal D Psychologie, NIG 6.Stock A0624
Wednesday 03.06. 16:45 - 18:15 Hörsaal D Psychologie, NIG 6.Stock A0624
Wednesday 10.06. 16:45 - 18:15 Hörsaal D Psychologie, NIG 6.Stock A0624
Wednesday 17.06. 16:45 - 18:15 Hörsaal D Psychologie, NIG 6.Stock A0624
Wednesday 24.06. 16:45 - 18:15 Hörsaal D Psychologie, NIG 6.Stock A0624

Information

Aims, contents and method of the course

[AIMS]
Decision neuroscience (aka, neuroeconomics) is one of the most interdisciplinary and fast-moving fields, as it combines psychology, economics, and neuroscience to gain insights into how the brain computes value and makes decisions in different contexts.

To this end, it is important to lay the foundation of main concepts and insights of decision neuroscience, so that students are able to deepen their knowledge and understanding in later stages of their studies.

[CONTENT]
This course is dedicated to introducing students to key findings in decision neuroscience as well as common research methods used in decision neuroscience. Throughout the course, we will also cover basic research skills and good scientific practice, e.g., open science.

[METHODS]
Oral presentations by lecturer and students, in-class participation, group presentations.

Assessment and permitted materials

[Assessment]
Regular participation: 20%
Individual paper presentation: 20%
Group Debate discussion: 15%
Group presentation: 20%
Group paper: 25%

Minimum requirements and assessment criteria

[Minimum requirements]
- Comfortable with understanding and presenting scientific content in English
- Basic knowledge about statistics (e.g., t-test, ANOVA, correlation, regression)

[Assessment criteria]
1: >90%
2: 81 - 90%
3: 71 - 80%
4: 61 - 70%
5: <=60%

Examination topics

- Able to provide a basic understanding of various topics in decision neuroscience
- Able to read journal publication and able to search for literature
- Able to tell the pros and cons of common neuroimaging techniques (EEG, fMRI, TMS, etc.)
- Able to appreciate the necessity of the open science practice
- Able to formulate valid research questions and design simple experiments

Reading list

[Journal articles]
- Ruff, C. C., & Fehr, E. (2014). The neurobiology of rewards and values in social decision making. Nature Reviews Neuroscience, 15(8), 549.
- Rangel, A., Camerer, C., & Montague, P. R. (2008). A framework for studying the neurobiology of value-based decision making. Nature reviews neuroscience, 9(7), 545.

[Books]
- Glimcher, P. W., & Fehr, E. (Eds.). (2013). Neuroeconomics: Decision making and the brain. Academic Press.
- Kahneman, D. (2011). Thinking, fast and slow. Macmillan.

[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.

Association in the course directory

70231

Last modified: Th 14.01.2021 18:28