Abschlussarbeiten
Themen für B.Sc. und M.Sc. Abschlussarbeiten werden hier ausgeschrieben und fortlaufend aktualisiert. Bei Interesse wenden Sie sich bitte mit einem kurzen Motivationsschreiben per Email an die jeweilige Ansprechperson (siehe Ausschreibung). Die Beschreibungen sind auf Englisch verfasst, die Abschlussarbeiten können aber auch auf Deutsch verfasst werden, sofern die jeweilige Ansprechperson damit einverstanden ist.
Neural mechanisms of decisions within and across domains
(with Sebastian Gluth; sebastian.gluth"AT"uni-hamburg.de)
The neuroeconomic standard view of how the brain realizes value-based decisions states that reward-related brain regions form a modality-independent representation of the subjective values of different options to allow comparing those values and choosing the option with the highest value. According to this view, it should not matter whether we make decisions between comparable options (i.e., within-domain decisions) such as choosing between two smartphones or between incomensurable options (i.e., across-domain decisions) such as choosing between a smartphone vs. a weekend holiday trip. In this fMRI study we challgenge this view and aim to show within- and across-domain decisions are associated with specific brain activation and connectivity patterns.
Master Thesis: Given the complexity of acquiring and analyzing fMRI data, this project is suitable for Master theses only.
Suggested literature:
Vlaev, I., Chater, N., Stewart, N., & Brown, G. D. A. (2011). Does the brain calculate value? Trends in Cognitive Sciences, 15(11), 546–554. https://doi.org/10.1016/j.tics.2011.09.008
Hayden, B., & Niv, Y. (2021). The case against economic values in the orbitofrontal cortex (or anywhere else in the brain). Behavioral Neuroscience, 135, 192–201. https://doi.org/10.1037/bne0000448
Unraveling other's preferences in bargaining through eye movements
(with Mrugsen Gopnarayan; mrugsen.gopnarayan"AT"uni-hamburg.com)
Eye movements are intricately coupled with decision-making, serving as a window into a decision-makers attentional processes. In social settings, we intuitively use this principle to infer other persons' preferences while they are deciding. According to previous work, showing the gaze allocation of one participant to another in a coordination game leads to them understanding each other's preferred choices better. In our current study, we are using this ability to test whether a seller can reach better agreements with a buyer if they can see their eye-movements in a cooperative bargaining setting. If successful, this study could have practical implications for real-world bargaining scenarios, such as sales pitches, negotiations, and online shopping interfaces.
For Bachelor’s Thesis: Conducting cooperative bargaining studies with interactive eye-tracking. Behavioral data analysis to answer a specific hypothesis (Example: Do sellers with access to eye-movement information get more reward?)
For Master’s Thesis: Conducting cooperative bargaining studies with interactive eye-tracking. Advanced Behavioral data analysis (using decision-making models and Game theory); possibly also analysis of eye-tracking data.
Suggested Literature:
Krajbich, I., Armel, C., & Rangel, A. (2010). Visual fixations and the computation and comparison of value in simple choice. Nature Neuroscience, 13, 1292–1298. https://doi.org/10.1038/nn.2635
Hausfeld, J., von Hesler, K., & Goldlücke, S. (2020). Strategic gaze: An interactive eye-tracking study. Experimental Economics, 24 (1), 177–205. https://doi.org/10.1007/s10683-020-09655-x
Neural underpinnings of predicting other people’s decisions
(with Erik Stuchlý; erik.stuchly@uni-hamburg.de)
When individuals are required to infer the mental state and predict the behaviour of another person, mental simulations are often involved – that is, the individual attempts to replicate the mental state of the other person with their own mind. However, as of now it remains unclear whether neural data supports this hypothesis, as well as whether the same/different mechanism is used to make predictions for different types of people (e.g. someone similar/dissimilar from oneself).
The current project will attempt to answer these (and other) questions by asking participants to make decisions for themselves and to predict the decisions of another person, while we record brain signals with EEG to elucidate the neural dynamics underlying the mental simulation process.
For Master thesis: Collecting data from an EEG experiment. Analysing behavioural and processed EEG data (possibly helping with pre-processing the EEG data) to answer specific hypotheses - e.g. do the neural signals differ during prediction for an agent with preferences that are similar, rather than dissimilar to participant’s own preferences?
For Bachelor thesis: Because of the complexity of collecting and analysing EEG data, this project is better suited for Master students. If interested, however, particularly motivated Bachelor students can support the Master students with the EEG data collection, and then analyse behavioural data to answer specific hypotheses - e.g., do people learn about the preferences of another person more quickly if said person is similar, rather than dissimilar from the participant?
Suggested literature:
Harris, A., Clithero, J. A., & Hutcherson, C. A. (2018). Accounting for taste: A multi-attribute neurocomputational model explains the neural dynamics of choices for self and others. Journal of Neuroscience, 38(37), 7952-7968.
Smith, S. M., & Krajbich, I. (2022). Predictions and choices for others: Some insights into how and why they differ. Journal of Experimental Psychology: General.
Dynamic adjustment of leaning and decision-making behavior
(with Rasmus Bruckner (rasmus.bruckner@fu-berlin.de)
Adaptive behavior requires dynamic learning processes. New information that can be relevant to our choices is often uncertain and ambiguous. For example, stock market data vary to some degree from day to day, and investment decisions should be made based on the average underlying price (e.g., averaged across a certain period). In other scenarios, the environment can change more fundamentally, such as after an economic crisis, which requires stronger behavioral adjustments (e.g., selling stocks to avoid losses).
In our project, we are interested in how humans adjust their learning and decision-making behavior to such uncertain and changing environments. We are currently developing a task that allows us to measure how humans dynamically regulate their learning behavior. This project is very well suited for a Bachelor's thesis, where students would work on a behavioral experiment with different versions of our task. Students would be involved in the data collection and learn behavioral analyses, also providing some insights into state-of-the-art computational modeling.
Suggested literature:
Bruckner, R. & Nassar, M. R. (2024). Decision-making under uncertainty. Accepted for publication in Encyclopedia of the Human Brain, 2nd edition (Academic Press). https://osf.io/preprints/psyarxiv/ce8jf
Nassar, M. R., Bruckner, R., & Frank, M., J. (2019). Statistical context dictates the relationship between feedback-related EEG signals and learning. eLife, 8:e46975. https://elifesciences.org/articles/4697