Movement is undoubtedly a critical aspect of everyday human life and understanding how humans plan, control, and learn skilled actions is an important line of inquiry for both fundamental and applied research. The Memory, Action, & Cognition lab at McMaster University aims to understand the cognitive and neural mechanisms that underlie human sensorimotor learning. We are particularly interested in how errors influence decision-making and strategy use to shape learning and skilled behaviour. Our research adopts a multidisciplinary approach that combines behavioural and neurophysiological techniques, and involves both healthy and clinical populations. Our findings are relevant for advancing fundamental knowledge in the area of movement neuroscience and for informing neurorehabilitation protocols.

The role of feedback

The underlying cause of errors in skilled actions is often ambiguous and difficult to assign. This assignment difficulty result from having to rely on noisy (i.e., uncertain) and delayed sensory information. Feedback from an external source (e.g., therapist, teacher, coach) can aid assignment processes. It is well established that allowing learners to decide when they receive such feedback, termed self-controlled feedback schedules, is more effective for learning than externally-imposed (i.e., yoked) feedback schedules. Yet, the mechanisms that underlie this robust learning advantage are not fully understood, with researchers adopting either a motivational or an information-processing explanation. We are currently testing between these explanations, with our work highlighting a greater role of information-processing factors over motivational influences. 

Decision-making for multiple-task learning

When we wish to learn something new we often have to take charge of our own training protocol. Learning a new musical instrument like the guitar involves learning multiple component tasks such as unique hand postures for different notes and chords. When learning alone, we must decide how to invest in learning all task variations. Although some work has examined this question, these studies have held the amount of training constant across each task. However, in our daily lives we do not always allocate an equal amount of time to each task we are trying to learn. Instead, we must make purposeful decisions about how to invest our time (e.g., whether to exploit known skills or explore less-known ones) to maximize overall proficiency. We are interested in the factors that affect our training choices and whether these decisions are optimal when the amount of training per skill can vary.

Assignment policies during motor interactions

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. During such collaborative interactions, the human user must plan and execute their actions in relations to what they anticipate the robotic device will do. We are interested in determining how humans interact with robots when they each have some independent control over the same control point.