We possess a remarkable ability to learn new motor skills and retain memories for those skills throughout life, such as riding a bicycle. The ease with which we perform these skills belies their overwhelming computational complexity. Our research focuses on unraveling the different computational processes involved in solving this motor control problem. A primary area of our research aims at understanding how explicit, cognitive strategies interact with implicit motor adaptation during skill acquisition. Specifically, how do novel movement strategies arise, what are the functional consequences of their interaction with learning, and what are their respective neural systems? Ultimately, we hope that this work can lead to the development of optimal training protocols that can guide learning towards different, but still functioning learning mechanisms following stroke or disease.
McDougle S.D., & Taylor J.A. (2019). Dissociable cognitive strategies for sensorimotor learning. Nature Communications, 10(1):40.
Hutter S.A., & Taylor J.A. (2018). Relative sensitivity of explicit re-aiming and implicit motor adaptation. Journal of Neurophysiology, 120:2640-2648.
Butcher P.A., & Taylor J.A. (2018). Decomposition of a sensory-prediction error signal for visuomotor adaptation. Journal of Experimental Psychology: Human Perception and Performance, 44(2):175-194.
Bond K.M., & Taylor J.A. (2017). Structural learning in a visuomotor adaptation task is explicitly accessible. eNeuro, 4(4).
McDougle S.D., Boggess M.J., Crossley M.J., Parvin D., Ivry R.B., & Taylor J.A. (2016). Credit assignment in movement-dependent reinforcement learning. Proceedings of the National Academy of Sciences, 113(24):6797-6802.