CHAIN Academic Seminar #10

Neural mechanisms of supervised versus reinforcement motor learning

Dr. Sungshin Kim (Center for Neuroscience Imaging Research, Institute for Basic Science, Sungkyunkwan University)

Date/Time: February 14, 2020, 16:00-17:00

Venue: Hokkaido University Conference Hall,Meeting Room3

Language: English

Most researchers agree on that motor learning can be categorized into two distinct types, motor adaptation and skill learning. They are differentiated whether learners recalibrate well-trained movement to changes in environment (adaptation) or generate novel movement patterns (skill learning). The motor adaptation involves a parametric change of a motor controller driven by sensory-prediction errors potentially computed by internal models. Thus, motor adaptation can be considered as supervised learning based on directional error feedbacks. In contrast, the motor skill learning involves generating novel movement patterns to achieve task goals. For the learning signals, the skill learning requires evaluative feedback such as reward or penalty instead of directional errors, thus involving initial exploration to select motor controllers associated with higher rewards. This type of learning is known as reinforcement learning. In this seminar, I compare and contrast supervised learning and reinforcement learning and present a fMRI study for each type of learning. I discuss their neural substrates, notably cortico-cerebellar network (adaptation) and cortico-striatal network (skill learning). For the former, I introduce simple computational models for motor adaptation with multiple time scales and present a fMRI study based on the model. For the latter, I present a de novo motor skill learning task for fMRI experiment recently developed in my lab, in which participants learned to control a computer cursor by moving their fingers from scratch. Lastly, I briefly introduce other ongoing studies and future research related with motor learning & memory in my lab.

Dr. Sungshin Kim

Center for Neuroscience Imaging Research
Institute for Basic Science Sungkyunkwan University

Sungshin Kim graduated from Seoul National University with double majors in electrical engineering and chemical engineering. In 2013, he completed PhD degree in neuroscience at the University of Southern California, Los Angeles, USA. After his PhD, he has worked as a postdoctoral scholar at the University of Chicago and Northwestern University. Now, he is a research professor and principal investigator of computational learning & memory neuroscience (CLMN lab) in the Center for Neuroscience Imaging Research located in Sungkyunkwan University. He was selected as a recipient of Young Scientists Fellowship in Institute of Basic Sciences (IBS) supported by Korean government. His lab pursue to understand neural mechanisms underlying human learning and memory, focusing on motor learning & memory. For this, he takes a combined approach of psychophysical experiment, computational modeling, fMRI, and neuromodulation such as transcranial magnetic stimulation. For more details, please visit the website of his lab at and