Professor Jonathan Tsay's Invited Talk on Sensorimotor Learning - June 2024

Tue, 04 Jun 2024 00:00:00 +0000

Professor Jonathan Tsay was invited to give a talk on June 4th at 12:30pm, which was held at Auditoire Maisin, Louvain-en-Woluwe. He is a distinguished young scientist in the field of motor control.

About Prof. Tsay

After completing his PhD at UC Berkeley under Rich Ivry, he pursued a postdoctoral fellowship at Cambridge with Tamar Makin. He is now establishing his own lab at Carnegie Mellon University in Pittsburgh.

Presentation

His presentation was titled “Behavioral, Neuropsychological, and Computational Perspectives on Sensorimotor Learning”, exploring the behavioral, neuropsychological, and computational dimensions of sensorimotor learning, with a special focus on his innovative work developing online experimental environments.

Following the talks, we enjoyed a delightful dinner with Prof. Tsay at a beautiful venue overlooking the scenic views of Brussels. The dinner provided an excellent opportunity for lively discussions and networking in a relaxed setting.

Motor Learning

Mon, 01 Jan 2024 00:00:00 +0000

Overview

Motor learning is the process by which movements can be improved through practice. It is of great practical relevance in daily life for a variety of situations such as learning to drive, to play sports, or music instruments. However, this process is equally important for patients who suffer from brain lesions such as stroke and who are left with motor impairment. In such cases, the quality of motor recovery depends on the ability to (re-)learn motor skills with the paretic hand. Better understanding the processes underlying motor learning is therefore crucial to optimize rehabilitation strategies and improve quality of life for these patients.

Learning Signals & Prediction Errors

A great deal of research has focused on the learning signals involved in motor learning. These works have emphasized the role of sensory feedback (visual, somatosensory) in acquiring new motor skills. A predominant view is that the brain learns by computing sensory prediction errors (SPEs)—the difference between received and expected sensory consequences of a movement.

However, recent research has shown that reward feedback can also strongly influence motor learning. The brain computes reward prediction errors (RPEs)—the mismatch between received and expected reward arising from a movement. These two types of error signals drive learning by allowing the brain to adapt motor commands based on sensory and reward information.

Research Goals

Behavioral & Neural Effects of Reward

Our project aims at better understanding the behavioral and neural effects of reward on motor learning in both healthy subjects and clinical populations. We investigate how reward signals modulate:

Motor skill acquisition - How reward accelerates or enhances learning of new movements
Motor performance - The immediate effects of reward on movement execution
Neural mechanisms - Brain areas processing reward during motor learning
Clinical applications - Using reward-based approaches to improve rehabilitation outcomes

Neurorehabilitation Strategies

With the ultimate goal of guiding future multi-approach neurorehabilitation strategies, we aim to optimize the integration of sensory and reward feedback for patients recovering from stroke and other motor impairments.

Motor Learning | CoActions Lab