My primary interest is the way algorithms that people use to maximise their reward shape the learned representation of environmental statistics. In other words, what properties of the environment do we learn depending on what decisions we have to make based on them. I build probabilistic models of perception and action to predict behaviour in experiments probing human statistical learning, and neural representations measurable with imaging methods. In particular, I'm interested in the effect of computational resource constraints on the learning of task-dependent stimulus representations. Previously I worked on modelling neural representations in the visual cortex. List of publications can be accessed here.
This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 897042, "Goal-directed learning of the statistical structure of the environment" (RELEARN) from 01.01.2021 to 31.12.2022, with EUR 174 806.40. See the project description on the CORDIS website of the EU.
2014-2019: postdoctoral researcher, Computational Systems Neuroscience Lab, MTA Wigner RCP, Budapest (Gergő Orbán)
2009-2013: graduate student, Computational Neuroscience Group of Péter Érdi, MTA Wigner RCP, Budapest (Fülöp Bazsó)
2007-2009: masters student, Robotics Lab, Pázmány University, Budapest (Gergely Feldhoffer)
2009-2013: PhD, Graduate School of Computer Science, Budapest University of Technology and Economics
2004-2009: MSc in CS/EE, Pázmány Péter Catholic University, Budapest, (with a semester at KU Leuven, Belgium)