Motor Control & Learning
Sensorimotor hand function can be described as a multidimensional space where mechanical, neural, and cognitive factors interact to enable a rich repertoire of actions – from playing musical instruments, to perceiving properties of our environment through exploratory procedures. Within this repertoire of actions, dexterous object manipulation is a hallmark of human evolution.
Co-adaptation of anatomical features and sensorimotor control mechanisms have made dexterous manipulation an effective means of interacting with the environment. Humans’ ability to perform complex hand - object interaction has also inspired research efforts aiming at building dexterous rehabilitation robotic devices.
Understanding he mechanisms underlying sensorimotor control and learning of grasping and manipulation is one of the core research thrusts of the NCML. We have been using a wide range of experimental protocols and behavioral approaches to determine fundamental principles underlying the coordination of multiple degrees of freedom, the interplay between sensorimotor memory and online feedback, as well as humans’ ability to learn and transfer skilled behaviors to novel contexts. Below are some examples of our research in this area.
Control of hand shape and multi-digit forces
One of the research thrusts of the NCML has been understanding how the hand’s multiple degrees of freedom are controlled. We performed a series of behavioral studies on the control of multi-digit joints for hand shaping and multi-digit forces. The work on whole-hand shape and five-digit grasp forces was based on earlier work by the NCML PI at the University of Minnesota. The work on hand shape revealed that the modulation of hand posture to object geometry can be captured by a few principal components, indicating significant covariation among joint excursions.
We conducted a series of studies to understand inter-related phenomena associated with sensorimotor learning of dexterous manipulation, i.e., transfer and interference. The key questions we focused on were: Can learned dexterous manipulation be transferred to a different context, e.g., object orientation or part of an object to be grasped?
Human-machine and human-human physical interaction
We are interested in determining the mechanisms underlying the control of physical interactions among two human agents. Unlike controlling one’s own limb, controlling the dynamic of an object that is also grasped by another subject is more complex as neither agent can accurately predict the mechanical and sensory consequences of his/her actions. In a collaboration with Dr. Panagiotis Artemiadis at Arizona State University, we found that human participants can infer the partner’s intended movement direction by probing his/her limb stiffness.