Our research goal is to better understand the neural basis of human perception and learning. We are interested in how we learn, both from a neural and computational point of view. We study the computational properties of machine learning algorithms and also investigate what physiological recordings and the constraints and limitations of human performance tell us about how our brains learn.
The driving philosophy behind our work is that studying both machine learning and human learning is synergistic. We use insights from human learning and brain physiology to guide machine learning algorithms and ideas from computational algorithms to guide studies of human and animal learning and computation. We believe in studying a question, not a technique, and so apply (including through collaboration) a wide range of techniques to address the question of how we learn to perceive the world around us.
The BCI Division of our lab implements this approach towards improving existing methods and discovering new ways of interpreting EEG data for BCI applications.
Velu, P. D. & de Sa, V.R. (2013). Single-trial classification of gait and point movement preparation from human EEG. Frontiers in Neuroprosthetics 7(84)
Robinson, A. E. & de Sa, V.R. (2013). Dynamic brightness induction causes flicker adaptation, but only along the edges: evidence against the neural filling-in of brightness. Journal of Vision 13(6)17, 1-14
Saygin, A.P., Driver, J., de Sa, V.R. (2008) In the footsteps of biological motion and multisensory perception: Judgments of audio-visual temporal relations are enhanced for upright walkers. Psychological Science, 19(5): 469-75.