My main research interest is the role of complex (beyond Gaussian) internal representations of stimulus statistics and other features of probabilistic computation in perceptual decision making. My PhD combines mathematical and computational modelling, machine learning techniques and psychophysical experiments.
At the beginning of my PhD I investigated learning and adaptation phenomena in sensorimotor timing tasks. In particular, in this paper we looked at the role of complex statistical properties of the context (i.e. non-Gaussian priors and non-quadratic loss functions, in Bayesian terms) in calibrating human sensorimotor timing.
My more recent work focusses on the sources of sub-optimality in human probabilistic inference within complex sensorimotor estimation tasks. At the moment I am systematically exploring the implications for behaviour of different assumptions regarding the decision process, sensory and decision noise and the internal representations of statistics (priors).