My broad research interest is perception, and in particular the notion that perception is a form of (possibly optimal) probabilistic inference ("Bayesian brain").

As part of my PhD work, I am investigating the acquisition and influence of perceptual expectations, which in a Bayesian framework are known as prior probabilities (or simply priors). My approach is both experimental (psychophysics) and theoretical (modelling - abstract and normative at first but later grounded to biologically plausible mechanisms). Work on expectations started with my MSc project at the DTC, where I investigated, through psychophysical experiments, how long-term priors on speed of visual motion can be manipulated and how they influence motion perception.

I am also interested in perceptual learning from a modelling perspective. By means of simulations, I am currently looking at the explanatory power of a certain type of models of early visual cortex called reweighting models.

One of the long-term objectives of my research is to unify the above two directions, ie delineate the link between perceptual learning and probabilistic inference.