Possible Bachelor and Master thesis projects
As of December 2023 we specifically offer theses connected to the following topics:
- Perception and simple generative models of visual scenes
- Adaptive experiments/sampling
- Causal impressions in and modeling of launching events
- Visual search in cluttered scenes
You can find more detailed information in the slides attached below. Please don't hesitate to also contact us with any other topics you would be interested in studying.
Presentation of theses topics from December 2023 (opens in new tab)
2024
- White did perception evolve to do? New approaches to test the interface theory of perception. (Niklas Gerlach, B. Sc.)
- Searching for shapes in a crowded visual search task: An analysis using probabilistic hierarchical models. (Moritz Kolvenbach, B. Sc.)
- Correlation of scene categories (Felix Graf, B. Sc.)
2023
- A Bayesian population coding model of anisotropies in orientation perception (Adrian Kühn, M. Sc.)
- Preference, expectation and tonality for short melodies (Kevin Nguyen, M. Sc.)
- A psychophysical evaluation of techniques for Mooney image generation (Lars Reining, B. Sc.)
- Orientation bar code structures for facial emotion recognition (Martin Schlink, B. Sc.)
- A neural style transfer approach to studying scene perception (Laura Goerke, M. Sc.)
- Human Similarity Judgements and their Representation in Psychological Space (Hannah Willkomm, B. Sc.)
- Interfacing VPIXX technologies hardware with PsychoPy (Florian Magin, B. Sc.)
2022
- Influence of Top-Down Scene Understanding on Sensitivity to Local Natural Image Structures (Ruth Hartmann, B. Sc.)
- Coherent dead leaves: Measuring precision and response time for detecting coherent structure in the presence of clutter and partial occlusion (Chris Riether, B. Sc.)
- Supramodal Regularities of Human Visual Behavior (Thomas Fabian, M. Sc.)
Prior to 2019 (mostly at the Uni Tübingen)
- Semantic and Contextual Influences on Peripheral Visual Sensitivity
- Predicting gaze position of humans watching videos
- Hole-in-the-wall: Comparing humans and deep neural networks in a spatial reasoning task
- A tutorial on Bayesian analysis of psychophysical data using Hierarchical models
- Using confidence reports to distinguish between (non) perceptual aftereffects
- Convolutional Neural Networks Represent Similar Mid-level Visual Features as the Ventral Visual Stream
- Distinguishing perceptual from decisional processes using confidence
- Comparing gamma calibration methods of PsychToolbox and PsychoPy
- Modelling classification images using Generalized Linear Models