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.

Bewerbungen werden ganzjärlich angenommen. Für allgemeine Anfragen schreiben Sie bitte eine Email an: cmdn-lab.psych"AT"uni-hamburg.de

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Choosing and rejecting to exert cognitive effort

(with Sebastian Gluth)

Assume you are asked to choose your preferred fruit out of two options, an apple or an orange. Now assume you are asked to select from the same two options the one you like less. In theory, the second decision should simply be a mirror of the first one (picking the apple is equivalent to rejecting the orange). However, previous research has indicated that choosing and rejecting have their own specific dynamics, and that people sometimes even choose and reject the same option (Shafir, 1993). In this eye-tracking project, we are studying search and decision dynamics when choosing and rejecting options with positive and negative attributes. Participants will be asked to choose or reject options of monetary rewards (positive attribute) that are obtained via exerting cognitive effort (negative attribute), while their eye movements are being recorded to study how they distribute attention among these attributes, depending on whether their goal is to select or de-select an option.

Bachelor and Master students can take part in this project for their theses, with the only difference being the amount of study support and the complexity expected of the student’s output. Thesis can emerge from analyzing behavioral data, eye-tracking data, and – if interested – applying cognitive modeling to these data.

References

Sepulveda, P., Usher, M., Davies, N., Benson, A. A., Ortoleva, P., & De Martino, B. (2020). Visual attention modulates the integration of goal-relevant evidence and not value. eLife, 9, e60705.
https://elifesciences.org/articles/60705

Shafir, E. (1993). Choosing vs. rejecting: While some options are both better and worse than others. Memory & Cognition, 21(4), 546–556.
https://link.springer.com/article/10.3758/BF03197186

Westbrook, A., Lamicchane, B., & Braver, T. (2019). The subjective value of cognitive effort is encoded by a domain-general valuation network. Journal of Neuroscience, 39(20), 3934–3947.
https://www.jneurosci.org/content/39/20/393

Neural mechanism of within and across domain choices

(with Maryam Tohidimoghaddam)

Everyday decisions can be grouped into two types: within-domain choices, where options share common characteristics (e.g., choosing between two vacation destinations), and across-domain choices, where options are fundamentally different and lack a basis for direct comparison (e.g., celebrating your birthday with friends vs. going on vacation).

Common decision theories posit that decisions are made by first assigning subjective values to different options and then selecting the option with the highest subjective value (Levy & Glimcher, 2012; Vlaev et al., 2011). Because options vary in attribute and domain (within vs. across), the brain must represent the value of different types of options on a common scale for comparison and choice (Perkins et al., 2024). However, opposing theories suggest that the representation of subjective value in the brain may be unnecessary (Hayden & Niv, 2021; Walasek & Brown, 2023). Previous research has proposed that subjective value is represented in the ventromedial prefrontal cortex (vmPFC) (Bartra et al., 2013; Padoa-Schioppa, 2011) and the orbitofrontal cortex (OFC). In this study, we first aim to investigate the value representation in within-domain and across-domain choices using the fMRI technique. We will simultaneously record gaze positions and behavioral signatures to further investigate the information search strategy in within-domain vs. across-domain choices.

Master and also Bachelor students can be involved in this project for their theses, the only difference being the expected complexity of the output. For your thesis, you will be expected to formulate hypotheses about the behavior of the participants and to analyze behavioral and eye-tracking data that has already been collected. You may also be expected to assist with some stages of fMRI data collection. Optionally, if you are interested, you may apply existing computational models to the data.

Dynamic adjustment of leaning and decision-making behavior

(with Rasmus Bruckner)

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

The role of overall value in the decision making

(with Chih-Chung Ting)

In addition to the task difficulty (e.g., difference between options), recent studies have shown that people tend to make faster decisions when choosing between two options with generally higher intensity or attractiveness compared to lower-intensity or less attractive options (Gluth et al., 2018; Shevlin et al., 2022). However, most research has focused primarily on decision speed, leaving open questions about how the overall value (OV) of available options influences the speed of information acquisition or/and processing (Beierholm et al., 2013; Korbisch et al., 2022).

This project aims to address these questions by systematically manipulating overall value (OV) and value difference (VD) in both perceptual tasks (e.g., brightness discrimination) and preferential tasks (e.g., snack choices). We have conducted a laboratory experiment combining behavioral and eye-tracking data. Bachelor and master students are welcome to join this project by analyzing existing eye-movement datasets or/and collecting new data to test research questions formulated in their theses. Master students, in particular, are encouraged to use computational models to develop hypotheses and analyze the data

References:

Gluth, S., Spektor, M. S., & Rieskamp, J. (2018). Value-based attentional capture affects multi-alternative decision making. eLife, 7, e39659.
https://doi.org/10.7554/eLife.39659

Shevlin, B. R. K., Smith, S. M., Hausfeld, J., & Krajbich, I. (2022). High-value decisions are fast and accurate, inconsistent with diminishing value sensitivity. Proceedings of the National Academy of Sciences, 119(6), e2101508119.
https://doi.org/10.1073/pnas.2101508119

Korbisch, C. C., Apuan, D. R., Shadmehr, R., & Ahmed, A. A. (2022). Saccade vigor reflects the rise of decision variables during deliberation. Current Biology, 32(24), 5374-5381.

Beierholm, U., Guitart-Masip, M., Economides, M., Chowdhury, R., Düzel, E., Dolan, R., & Dayan, P. (2013). Dopamine modulates reward-related vigor. Neuropsychopharmacology, 38(8), 1495-1503.

The affective bias on confidence and attention allocation in the learning task

(with Chih-Chung Ting)

Confidence plays a critical role in shaping our strategies for processing information and belief updating. For instance, individuals tend to search for less information when they feel more confident about making a correct decision (Desender et al., 2019). However, confidence can be biased by the anticipated outcome (e.g., rewards vs. losses). A growing body of research has shown that confidence is generally higher when learning to gain rewards compared to avoiding losses, despite similar learning performance in both contexts (Lebreton et al., 2019; Ting et al., 2023). This raises two questions: (1) Whether fixation pattern is also biased by the valence of outcome? (2) To what extent does valence-induced confidence bias can explain the variance of fixation patterns during outcome evaluation?

This project aims to investigate the relationship between fixation patterns, task performance, and confidence across different learning contexts. To address these questions, we will use a learning task in which potential outcomes are manipulated as rewards or losses, paired with eye-movement measurement (or manipulation). Both bachelor and master studentsstudents are welcome to join this project, and will be expected to assist with setting up the experiment, collecting data, and analyzing the results.

References:

Desender, K., Murphy, P. R., Boldt, A., Verguts, T., & Yeung, N. (2019). A post-decisional neural marker of confidence predicts information-seeking in decision-making. Journal of Neuroscience, 17(2620), 3309–3319.
https://doi.org/10.1101/433276

Lebreton, M., Bacily, K., Palminteri, S., & Engelmann, J. B. (2019). Contextual influence on confidence judgments in human reinforcement learning. PLoS computational biology, 15(4), e1006973.

Ting, C. C., Salem-Garcia, N., Palminteri, S., Engelmann, J. B., & Lebreton, M. (2023). Neural and computational underpinnings of biased confidence in human reinforcement learning. Nature Communications, 14(1), 6896.

Making Smart Choices: A combined EEG and eye-tracking study on value-based decision making.

(with Jordan Deakin)

Shopping can be enjoyable, but when faced with countless options, how do we efficiently decide on what to buy? Every product has multiple attributes—such as screen size, storage, and price when buying a phone—that must be evaluated and integrated to make the best choice. Recently, we proposed a Bayesian computational model that predicts how humans efficiently allocate their attention in order to make these choices. The model suggests that individuals sample information about each attribute over time, using it to continuously update their overall belief in an option’s value. A choice is made once a person is sufficiently confident that one option is more valuable than the others.

This project aims to further investigate the cognitive mechanisms involved in this decision-making process. To validate the model's assumptions, we will employ a combination of eye-tracking and EEG to capture decision-making in real time. Participants will complete a task in which they choose between different options, while eye movement and EEG data are recorded. Open to Bachelor’s and Master’s students, this project provides valuable hands-on experience with the collection and analysis of EEG and eye-tracking data, as well as the opportunity to apply computational modelling to interpret the findings.