200005 PS Introductory Seminar Biological Basis of Experience and Behaviour (2021S)
Continuous assessment of course work
Labels
REMOTE
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).
- Registration is open from Tu 02.02.2021 09:00 to We 24.02.2021 09:00
- Deregistration possible until We 03.03.2021 09:00
Details
max. 40 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
[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.
[Language] This course will be taught in English![Good to know] See below to find out if this course is for you.Example slides: https://bit.ly/2U48Mkh
Previous year's recording: https://www.youtube.com/playlist?list=PLfRTb2z8k2x9HGbU47CuRxpDDkhpr-4ZZ
Friday
19.03.
09:45 - 11:15
Digital
Friday
26.03.
09:45 - 11:15
Digital
Friday
16.04.
09:45 - 11:15
Digital
Friday
23.04.
09:45 - 11:15
Digital
Friday
30.04.
09:45 - 11:15
Digital
Friday
07.05.
09:45 - 11:15
Digital
Friday
14.05.
09:45 - 11:15
Digital
Friday
21.05.
09:45 - 11:15
Digital
Friday
28.05.
09:45 - 11:15
Digital
Friday
04.06.
09:45 - 11:15
Digital
Friday
11.06.
09:45 - 11:15
Digital
Friday
18.06.
09:45 - 11:15
Digital
Friday
25.06.
09:45 - 11:15
Digital
Information
Aims, contents and method of the course
Assessment and permitted materials
Regular participation: 20%
Individual paper presentation: 20%
Group Debate discussion: 15%
Group presentation: 20%
Group paper: 25%
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%
- 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
[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
- 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]
- 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.
- 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.
- 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., Caceres, A. S. J., Oakley, B., ... & den Ouden, H. (2019). Modeling cognitive flexibility in autism spectrum disorder and typical development reveals comparable developmental shifts in learning mechanisms.
- 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.
- 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]
- 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.
- 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.
- 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., Caceres, A. S. J., Oakley, B., ... & den Ouden, H. (2019). Modeling cognitive flexibility in autism spectrum disorder and typical development reveals comparable developmental shifts in learning mechanisms.
- 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.
Association in the course directory
70231
Last modified: Fr 12.05.2023 00:19
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.