Susan Coltman

My interest in motor learning and control comes from 15+ years as a world-ranked heptathlete in track and field. That experience gave me a firsthand understanding of how different types of feedback (visual, proprioceptive, and tactile) shape skill, and how each person relies on them in unique ways. This question has driven my academic career: how does the brain weigh and combine feedback during motor learning?

I earned my PhD in neuroscience from Western University, where I used computational modeling and behavioral analysis to study short-term motor adaptation. During my postdoctoral training, I took this work in two directions. At the University of Colorado, I studied how skilled reaching develops by recording muscle activity in mice. At Penn State, I explored how the brain uses artificial sensory feedback for prosthetic and neurorehabilitation purposes, combining fNIRS neuroimaging, EMG, and Bayesian computational modeling.

I am an Assistant Professor in the Department of Psychology at the University of Guelph. My research focuses on how the brain adjusts feedback use in sensorimotor control, using approaches like Bayesian integration, optimal control theory, and predictive coding. I aim to understand how feedback reliance changes with learning, task demands, and individual differences. My long-term goal is to use this knowledge to develop personalized interventions for motor learning and rehabilitation, especially for older adults and stroke survivors.

I especially enjoy the early problem-solving stage of research, when I figure out which questions a task can truly answer, and the writing process, where the story of the data comes together. I bring the same analytical drive I had as an athlete to my research. I am comfortable with rigorous self-assessment and believe that understanding individual differences in learning is the key to improving feedback-based training.