Title: Cortical mechanisms underlying the integration of memory and sensory feedback for dexterous hand-object interactions.

Speaker: Marco Santello, Ph.D. School of Biological and Health Systems Engineering Arizona State University

Time: May 31 2017 (Wed) 14:30-16:30

Venue: Room 1113, Wang Kezhen Building

Sensorimotor control and adaptation rely on complex interactions between reflexes and anticipatory control mechanisms. Through repeated exposure to mechanical interactions with the environment, the sensorimotor system learns to expect sensory consequences arising from motor actions, whereas reflexes would intervene when a discrepancy occurs between expected and actual sensory feedback. We have tested this theoretical framework in the context of grasping and dexterous manipulation using tasks that allow subjects to explore and choose relations between digit forces and positions. This experimental approach has revealed the phenomenon of digit force-to-position modulation, through which trial-to-trial variability in digit placement is compensated for by digit force modulation. However, this phenomenon has also opened up questions about what the neural representations underlying learned manipulation are, and in particular the relation between high-level (task) and low-level (effectors) representations. To address this question, we have designed experiments that combines behavioral and electrophysiological approaches (non-invasive brain stimulation and electroencephalography) to identify cortical mechanisms underlying learning and execution of dexterous manipulation. I will review experimental evidence supporting the following notions: (1) Interactions with objects grasped at unconstrained versus constrained contacts are mediated by different sensorimotor mechanisms, (2) high-level representations of learned manipulations acquired through sensorimotor adaptation allow the central nervous system to compensate for motor variability, but (3) high-level representations can also limit the extent to which a given learned manipulation can transfer to different task contexts. These findings extend our understanding of the functional role of cortical areas within the grasp network and have implications for neuroprosthetics, robotics, and rehabilitation of sensorimotor function.