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.
Al-Fawakhiri N., et al. (2023). On the money and right on the target: How robust are reward and task success for implicit motor adaptation. bioRxiv.
Annes, C.K., et al. (2023). The effect of workspace tidiness on schoolwork performance of high school students. Journal of Emerging Investigators, 6:1-5.
Tsay, J. S., et al. (2023). Strategic Processes in Sensorimotor Learning: Reasoning, Refinement, and Retrieval. PsyArXiv.
Velázquez-Vargas, C. A., Daw, N. D., & Taylor, J. A. (2023). Learning generalizable visuomotor mappings for de novo skills. bioRxiv.
Velázquez-Vargas, C. A., & Taylor, J. (2023). Exploring human learning and planning in grid navigation with arbitrary mappings. Proceedings of the Annual Meeting of the Cognitive Science Society, 45(45).