Our research group seeks to understand how we perceive the visual world around us and interact with it in a meaningful manner. To this end, we acquire and analyze large-scale behavioral and neuroimaging datasets in humans. This allows us to identify meaningful and highly reproducible structure that informs us about the visual cognitive architecture. We complement this approach with data-driven computational models (e.g. deep convolutional neural networks) that allow us to derive testable predictions from such large-scale datasets.
Available PhD projects
The Vision and Computational Cognition Group focuses on visual cognition using images of everyday objects. Available PhD projects with strong potential are:
- Identifying stable core dimensions underlying the mental representation of objects (online crowdsourcing, computational modeling, neuroimaging)
- Revealing the relationship between variables in diverse computational models of vision and cognition and large-scale neuroimaging data (computational modeling, neuroimaging)
- Improving the spatial and temporal resolution and sensitivity of functional neuroimaging methods (fMRI, MEG, fMRI-MEG fusion) using ultrafast MR sequences and novel analysis methods (neuroimaging, machine learning)
- Developing computational models of vision and semantics using knowledge from human brain imaging and behavior (computational modeling, machine learning)