RTG 2906 Curiosity

Laura Höber

I completed my Bachelor's degree in Biomedical Engineering at Graz University of Technology, earning a place on the Dean's List for the top 5% of graduates. Continuing my Master’s studies in the same program, I specialized in computational neuroscience, with a focus on machine learning and biosignal processing to investigate neural signals and their use in brain-computer interfaces. This specialization also shaped my Master's thesis, "The Influence of Breathing on the Electroencephalogram (EEG) and Electrocardiogram (ECG) during Motor Execution”. In this work, I investigated how respiration, cardiac dynamics, and neural activity interact during movement.


Information Theoretic Models of Curiosity in Hierarchical Models of the World

My PhD project investigates how computational models based on information theory and reinforcement learning can explain human curiosity and information-seeking behavior. To connect theory with observed behavior, these approaches will be tested and refined using real data from computer-game-based experiments with participants across different developmental stages. Ultimately, the project aims to establish a computational benchmark for competing models of curiosity. This will help identify which best captures the balance between exploring new information and exploiting existing knowledge.


I originally wanted to understand the brain by measuring neural signals and decoding intended actions directly at the source in brain-computer interfaces. The more I learned, however, the more I became interested in the computations that give rise to those actions before they can be measured. After all, every decoded intention begins as a decision, and one of the most fundamental decisions we constantly make is what information is worth exploring. Perhaps nowhere is this more visible than in children: they learn about the world by asking endless questions, climbing on things they shouldn't, and occasionally poking beetles with sticks. Remarkably, this seemingly chaotic behavior turns out to be an incredibly successful learning strategy. What interests me most is understanding the computational principles behind this process, and how they might help to explain biological intelligence and inspire AI that learns more like humans.



  • Research School 2025 “Games 4 Health”, Université Grenoble Alpes.
  • Winter School 2025 “Mixed Reality in Medicine”, FH Technikum Wien.