My research is focused on how we learn about structure and predictability in the sensory environment and how this shapes our performance on perceptual decision making tasks. In particular, I'm interested in how activity in populations of neurons encodes features of the environment, how this stimulus-specific activity is propagated through distributed neural circuits in order to generate decisions, and how learning modulates these circuit interactions to represent relevant prior experience and reflect changing task parameters and attentional goals. To study these questions, I use convergent approaches from both theoretical and experimental neuroscience in order to understand the underlying computations enacted by neural circuits during perceptual learning and decision making.
More specifically, I use psychophysics and EEG to look at the correlation between stimulus features, behavioural performance and the properties of large-scale cortical activity during simple perceptual tasks. In parallel, I use insights from this experimental work to develop biophysically plausible models of the patterns of neural activity that emerge from the interaction between sensory input, intrinsic neural dynamics and distributed circuit interactions in order to understand how systems of neurons adaptively perform perceptual processing in the brain.
At present, I am studying the perceptual processing of tactile vibrations in the somatosensory system. I have conducted a number of expeirmental and theoretical studies on the interaction between intrinsic neural activity and periodic sensory input. More recently, I am working on adaptive neural circuit models of perceptual decision making that can account for the influence of prior sensory experience on performance in sequential vibrotactile discrimination tasks.