# **Sensorimotor Learning Lab** # #### ***Announcements:*** - **The lab will be relocating to its new home at the University of British Columbia in the School of Kinesiology in July 2023 (read more [here][1]) and recruiting new graduate students for next Fall. This is an exciting transitional period for me (Hyosub). I say 'me' instead of 'we' because all of the wonderful trainees I've had the pleasure of working with while at UD will either be graduating by then or need to stay at UD for personal reasons. This means I am building my new lab at UBC up from scratch, making it the perfect time to get in touch if you have an interest in studying human motor learning and want to work at a great institution while living in a vibrant, beautiful city. Find out more on the Open Positions page, and please contact Hyosub if you are interested.** - **We've developed a new open-source Python library for utilizing Bayesian statistics. Check out the Bayesian Statistics Toolbox (BST) [here][2]!** Research in our lab focuses on how humans acquire, retain, and improve skillful movement. We combine motor psychophysics, computational modeling, and patient testing in order to tackle these topics. Current projects aim to examine interactions between different forms of implicit motor learning, the effects of practice on movement planning, and the nervous system’s integration of visual and somatosensory information during reaching. Ultimately, we hope that this work will produce fundamental knowledge regarding motor control and learning that is used to advance neurorehabilitation practice.   We are grateful to the following agencies for their generous support: - NIH/NICHD/NINDS - [NSF][3]   If you have just joined the lab, head over to the [Lab Manual][4] page and check out our [Resources][5].   Interested in participating in a study? Contact us at *hyosub@udel.edu*      **We are located on the University of Delaware's STAR campus:** STAR Health Sciences Complex (Room 115) Department of Physical Therapy University of Delaware 540 S. College Ave. Newark, DE 19713 [1]: https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=505402 [2]: https://github.com/hyosubkim/bayesian-statistics-toolbox [3]: https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=505402 [4]: https://osf.io/jw7f4/ [5]: https://osf.io/y75ud/wiki/Resources