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Research Area - Motor Learning

Motor Learning

With respect to the research topic of “motor learning,” the nemolab is generally addressing basic questions in this field. How do we learn to handle the uncontrolled variability in our motor system that leads us to make errors? Which roles do the prediction of errors and the processing of errors play in this? How do we integrate varying and sometimes conflicting information from different sensory channels? We are addressing these questions with both virtual and naturalistic tasks in which we analyze kinematic and neurophysiologic processes.

As part of this research topic, we are investigating the role of predictive error valuation in learning complex motor tasks with the DFG funded project “Predictive Error Perception: Neural correlates and development in complex motor tasks” (Project B6 in SFB TRR135). The project combines electrophysiological experiments with modeling to gain further insights into predictive error perception and valuation during action execution by analyzing event-related potentials in the EEG. More specifically, we aim to investigate how (a) predictive error perception develops during the course of learning, (b) information from different action-related sources of information is integrated into predictive error perception, and (c) learning of complex sensorimotor transformations can be augmented through an individual’s ability to predictively perceive and evaluate action outcome errors.