Research in the NIMBL uses a combination of behavioural, neuroimaging, and brain stimulation techniques. Projects span basic and applied neuroscience and are related to three streams:
Understanding mechanisms of non-physical forms of practice
Optimizing schedules and parameters of non-physical practice
Developing effective interventions using non-physical practice for neurorehabilitation
Can we detect and correct errors through imagined practice?
Repetitive practice drives changes in the brain, which leads to learning new skills. Each time we practice a skill, information about the outcome of the movement is compared to our original plan for the movement. Comparing this information allows us to detect errors, so that we can correct these errors the next time we practice the skill. While physical practice is the ‘gold standard’ of learning’, we can also imagine ourselves performing the task without moving (called motor imagery) to learn new skills. However, when we imagine performing skills, we are not actually moving. This means there is no way to compare the outcome of a movement to our original plan. The goal of this study is to determine if we can still detect and correct errors in our imagined performance using an alternate comparison. Results of this study will help us better understand how we learn skills through imagined practice.
Project team: Tarri Jessey, Soumyaa Subramanium
Scheduling imagined practice to improve learning
Motor imagery is the mental rehearsal of movement and allows us to learn new or improve upon existing motor skills. When combined with physical practice, it can lead to greater learning. However, little is known about how motor imagery should best be scheduled with physical practice to drive learning. The goal of this study is to determine how motor imagery and physical practice should be combined to maximize learning. Results of this study will help us understand how motor imagery should best be used to learn skills.
Project team: Harpreet Jaswal, Alexa Wong
Tracking learning and re-learning, in and out of the lab
When tracking learning and re-learning of skills, it is vital to gather information about how we are moving. This information is called kinematic data. For instance, kinematics can include speed, joint angles, and rotation. Motion capture systems are used to capture kinematics, which typically involve cumbersome and expensive, laboratory-based setups. Further, many systems lack the ability to capture both the small movements of button presses and grasping, or the larger movements of dancing. Recently developed technology uses machine learning to track movements. While this technology is promising, and allows us to track learning of more ecologically valid movements or movements performed outside of a laboratory setting, the specificity and accuracy of these systems has not been studied. The goal of this study is to test this technology across a wide range of fine motor skills and gross motor tasks. The results of this project will permit learning to be captured in a wide range of settings and studies, improving our ability to understand how learning occurs.
Project team: Conducted in collaboration with the Brain Behaviour Lab and the Motor Skills Lab (UBC-V), via the Multidisciplinary Research Program in Medicine