Our research group (the SensorimotorLab) aims to uncover the computational and neural strategies underlying human sensorimotor processing through modeling, psychophysics, and neuroimaging. We focus on how sensory information transforms into spatial representations and motor actions, as well as how these representations are learned, updated, or maintained during self-motion.
Optimal control and Bayesian models are developed to guide our research. We employ advanced tools such as a vestibular motion platform, robotic manipulandums (vBot, 3Bot), virtual reality systems, and kinematic recording techniques (Eyelink, Optotrak) for behavioral experiments. Neuroimaging methods (fMRI, EEG, MEG) and perturbation techniques (TMS, tDCS, GVS) help us identify the neural circuits involved in sensorimotor integration, particularly in the cerebral cortex. Adopting a systems neuroscience approach, we also apply our paradigms in clinical settings to study sensorimotor deficits.
Recently, using methods from data science and artificial intelligence, we have started to bridge laboratory research with real-world applications. This expansion contributes to the emerging field of Naturalistic Neuroscience.
