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
Current Projects:
How does the brain communicate during practice?
We learn and improve skills through repetitive practice, plus feedback about our performance. Practice which can take place in several forms (e.g., physical, observing someone else, imagining performing movement). While it is known that the hemispheres of the brain communicate during physical practice, whether this occurs similarly during observation or motor imagery practice is not well understood. This study aims to examine how the hemispheres of the brain communicate with each other when we engage in different forms of practice. The overall goal is to improve our understanding of the use of non-physical forms of practice in learning.
Project team: Beier Lin, Kelly Spriggs, Kyle Vallido
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, Beier Lin
Make your prediction! How much is too much, and how little is too little?
Mental practice broadly refers to the rehearsal or visualization of a skill and are often used in a sport setting. Mental practice can result in positive effects on athletic performance, but less is known about the impact specific forms of mental practice (e.g., motor imagery) has on an athlete’s ability to predict movements in sport. While athletes with more experience are typically more accurate when making these predictions compared to novices, whether or not incorporating imagery in training impacts these skills is unknown. This study aims to identify the link between the frequency and type of mental practice used in training, and action prediction of a volleyball serve. In this study we are also examining how one’s level of expertise relates to action prediction.
Project team: Parres Holliday, conducted in collaboration with Dr. Karlinsky (CSUSB) and Dr. Yousef (UBCO)
What we imagine we learn when we watch others
Observing someone else perform a task is one of many ways to help ourselves learn a skill. However, when we observe someone else perform a task, our confidence in performing the task ourselves is often much higher than our actual skill level. Unlike observation, when we mentally rehearse the skill ourselves (termed ‘motor imagery’) we are gaining movement-related information. This information may lead to greater accuracy in assessing ability and thus a match between our confidence and skill level. The goal of this study is to look at how motor imagery changes our confidence and perceived ability to perform a novel task.
Project team: this work is conducted in collaboration with Dr. Hodges & the Motor Skills Lab, UBC.
Gamifying neurorehabilitation: can we predict who will benefit?
Stroke often leads to impairments in hand and arm function. Gamified rehabilitation is one way to provide individuals with stroke additional opportunities to improve hand and arm function. However, while many benefit from these interventions, others do not. The goal of this study is to find out why, by mapping brain function and structure before and after practice to determine who benefits.
Project team: this work is conducted in collaboration with Dr. Boyd & the Brain Behavior Lab, UBC.
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: Vaidehi Wagh, Alisha Davis
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