Sensory and motor plasticity in learning

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The motor-control systems in our brain are remarkably good at rapidly adapting to continuous changes in our environment and changes in our body. Yet, the mechanisms that underlie such sensorimotor learning remain largely unknown. The aims of this proposal are to 1) better understand the sources of information used to flexibly learn and adapt movements, 2) to explore processes in the brain that underlie sensorimotor learning, and 3) to investigate how this extends to interacting with tools and other objects. Being able to adapt to changes in our environment and body relies on our ability to estimate the location of our limbs, in particular, our hand. This is called state estimation and is a key element in all models of motor control. A state estimate combines sensory feedback with information related to outgoing motor commands (efferent copies) used by forward models in the brain to predict the sensory consequences of the outgoing motor command. Yet, most models of motor control assume that adaptation exclusively results from updates in prediction, but not updates in perception. Work from my lab shows that proprioceptive estimates of the hand do change during learning and partly account for changes in motor performance. To better understand how and to what extent both proprioception and prediction contribute to motor learning, we have developed a method for isolating their distinct contributions. In the first aim of the proposed research, we will test the sensitivity of each to multiple factors known to impact motor learning, as well as measure and model their separate time courses throughout learning. In the second aim, we will complement these behavioural and modeling studies with electroencephalography (EEG) to identify brain activity reflecting ongoing processing of sensory and prediction errors and the resulting changes in state estimation and motor planning throughout motor adaptation. Motor learning does not only involve forming and updating forward models of our effectors, but also building motor-related representations of objects and tools we interact with. Yet, this aspect of learning has been largely overlooked even though the diversity and complexity of human movements involves the ability to predict and skillfully manipulate objects. Thus, for our third aim we use 3D virtual reality (VR) technology to manipulate the normal physics associated with operating familiar tools and objects to characterize tool-skill acquisition. The proposed research will expand our understanding of human sensorimotor learning, including the underlying brain activity and skilled tool-use. This in turn could lead to improved rehabilitation, better human-machine interfaces, and better motor-skills training for all levels of performance.