Decision-making and assignment policies in sensorimotor learning

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Learning from the consequences of our actions is the cornerstone of sensorimotor learning and human cognition. Thus, accurately specifying the source of errors (i.e., assignment) and initiating, from one movement to the next, the necessary adjustments (i.e., decision-making) is critical for adaptive, goal-oriented behaviour. Yet, the mechanisms and strategies that allow us to respond to errors—which are essential to understanding the decision-making processes that underpin sensorimotor learning—are poorly understood. The broad aim of the proposed research is to better understand how we incorporate information about motor errors into decisions that shape sensorimotor learning.******While it is recognized that the best way to learn new motor skills is through extensive training, we know not all forms of practice are equally effective. There is converging evidence that self-controlled feedback schedules are more effective for motor learning compared to experimentally-imposed schedules. However, the mechanisms underlying this learning benefit are not well understood. Project 1 will test between two competing explanations for this benefit to better understand the cognitive and neural mechanisms of self-controlled learning advantages.******Real-world action tasks involve learning a number of task variations, such as different hockey shots (e.g., slapshot, backhand, wristshot). While a coach can tell us how to practice these variation, the reality is that most of our training time is spent alone. Project 2 will investigate the factors that affect our training choices during multiple task learning. Unlike previous research, a key aim of this project is to determine if these choices are optimal when the amount of training between skills can vary.******Motor interactions between multiple agents is common in everyday life. In recent years, the prevalence of assistive robots has increased in a variety of rehabilitative and training settings (e.g., surgery) where the human and robot collaborate to achieve a common goal. This redundancy in coordination poses an appreciable computational challenge during learning. That is, the human must assign the error to a source even though the true source (i.e., self, robot, combination) is ambiguous. Project 3 will investigate the effectiveness and optimization of human-robot interactions for sensorimotor learning.******The proposed research will contribute to our understanding of how the brain controls and learns real-world action tasks. Specifically, we will gain greater insight regarding the complex interplay between motor errors and decision-making. Having a solid understanding of these mechanisms may be able to promote and facilitate learning in workplace, surgical, and sport settings.