PhD projectFalse Clarity: How AI-Enhanced Images Affect Neural Face Processing and Forensic Face Matching
I’m interested in how the visual system builds useful, structured representations from noisy or ambiguous inputs. What fascinates me is that perception is a constructive process: the brain filters out noise and fills in gaps to give us a stable representation of the world. I like using computational models to make these hidden cognitive processes explicit and testable.
Before joining the CCN lab, I completed a BSc in Psychology (Brain & Cognition) and a Research MSc in Cognitive Neuroscience at Radboud University and the Donders Institute. During my master’s thesis in the Predictive Brain Lab, supervised by dr. Floris de Lange and dr. Eva Berlot, I studied how ambiguous, continuous visual input is transformed into categorical representations across the visual hierarchy, using fMRI and deep neural networks. This sparked my interest in how the visual system deals with uncertainty, which I now study in a more applied, forensic setting.
In my PhD project, supervised by dr. Mario Senden, dr. Anna Sagana and dr. Judith Peters, I look at how AI-enhanced face images affect face processing and forensic face matching. AI tools are increasingly used to enhance low-quality surveillance images, making faces appear clearer, but not necessarily easier to identify correctly. I investigate how this affects viewing strategies, neural representations and recognition decisions, combining biologically grounded models of active vision with behavioural experiments, eye-tracking and intracranial EEG.
Outside of work, I enjoy hiking, going to concerts and museums, cooking and long dinners with friends.