Our lab aims to shed light on fundamental principles of human motor control and learning during both health and disease. To this end, we develop theories that are formalized, tested, and refined by combining computational modeling and behavioral experiments. We strive to practice open science and will develop open-source software to improve scientific rigor, regardless of discipline. All of this will be accomplished in a welcoming and collaborative environment that serves as a training ground for a diverse group of creative scientists.
How are humans able to acquire, retain, and adapt a seemingly limitless repertoire of skilled movements across the lifespan? Our lab aims to address this question and thereby shed light on fundamental principles of learning and memory in the healthy and diseased human motor system. Towards this aim, we combine theory with motor psychophysics, computational modeling, and patient testing. Our research into motor learning encompasses a wide range of behaviors, most prominently, the control of goal-directed reaching, an ideal model system to understand interactions between cognition and action given that reaching encompasses high-level decision-making and low-level automatic processes.
Some important questions for the lab include:
Our behavioral experiments incorporate virtual reality and robotics so that we can carefully manipulate the visual and somatosensory information provided to participants, all in a gamified environment. In this manner, we can assess unperturbed reaching as well as responses to different types of error signals. By precisely controlling the inputs to, and measuring the outputs from, the system (in this case, a human participant), we make inferences about the computations being performed by the brain.
We use modeling to formalize, test, and refine our theories of the computations involved in learning and adapting motor skills. We apply different modeling approaches (e.g., Bayesian, reinforcement learning, state-space models) depending on the question. The behavioral data we collect often serve as a testbed for our models. In a complementary manner, our models also help generate new hypotheses and future experiments that advance our understanding of the processes under investigation.
We are interested in studying the impact of neurologic disorders, such as Parkinson’s disease, stroke, or cerebellar ataxia, on motor control and learning. We believe that increasing our fundamental knowledge of these diseases is a pathway to more sensitive behavioral assessments and targeted therapeutic interventions.