Improved performance in complex motor behaviors (such as sports and playing musical instruments) requires multiple learning processes; each one of them is associated with distinct learning algorithms, neural substrates and retention properties. The goal of our research is to understand this modularity in motor learning and in motor control using behavioral experiments, computational modeling, and advanced functional Magnetic Resonance Imaging (fMRI) techniques.
We recently showed that motor skill learning (defined as improvement in task’s execution) is associated with improved feedforward and feedback control processes. We currently study the time course of acquisition and generalization of each control mechanism and their underlying neuronal substrates. Future work will utilize generalization and retention properties to improve rehabilitation protocols.
Another level of investigation addresses the learning algorithms of motor learning. Accumulating results indicate that error-based and reinforcement learning mechanisms operate together during motor learning. Current research addresses the interaction between these mechanisms and their neural substrates during adaptation learning using multivariate and effective connectivity fMRI methodologies. We also investigate the cooperation between model-based and model-free learning mechanisms during Brain Computer Interface (BCI) protocols.
Last, we are actively searching for ways to translate the basic science knowledge that we acquire to neurorehabilitative treatments. The lab is part of the Aleh-BGU Negev Translational Neurorehabilitation laboratory at Aleh Negev, Nachalat Eran, where we develop quantitative motor and cognitive assessment tools (using markerless 3D motion tracking and computerized cognitive assessments), examine the effect of novel interventions that involves high-dose emmersive technologies on stroke recovery and study the neural correlates of stroke impairment and recovery. For more information, visit - https://www.negevlab.com/.