Biomedical subjects

Christopher D Mah

Publications and source records attributed to Christopher D Mah.

4 recordsLinked to original sources

PubMed2003

Experimentally confirmed mathematical model for human control of a non-rigid object.

Determining the principles used to plan and execute movements is a fundamental question in neuroscience research. When humans reach to a target with their hand, they exhibit stereotypical movements that closely follow an optimally smooth trajectory. Even when faced with various perceptual or mechanical perturbations, subjects readily adapt their motor output to preserve this stereotypical trajectory. When humans manipulate non-rigid objects, however, they must control the movements of the object as well as the hand. Such tasks impose a fundamentally different control problem than that of moving one's arm alone. Here, we developed a mathematical model for transporting a mass-on-a-spring to a target in an optimally smooth way. We demonstrate that the well-known "minimum-jerk" model for smooth reaching movements cannot accomplish this task. Our model extends the concept of smoothness to allow for the control of non-rigid objects. Although our model makes some predictions that are similar to minimum jerk, it predicts distinctly different optimal trajectories in several specific cases. In particular, when the relative speed of the movement becomes fast enough or when the object stiffness becomes small enough, the model predicts that subjects will transition from a uni-phasic hand motion to a bi-phasic hand motion. We directly tested these predictions in human subjects. Our subjects adopted trajectories that were well-predicted by our model, including all of the predicted transitions between uni- and bi-phasic hand motions. These findings suggest that smoothness of motion is a general principle of movement planning that extends beyond the control of hand trajectories.

Algorithms↗

PubMed2003

Generalization of object manipulation skills learned without limb motion.

Recent work suggests that human subjects may learn mappings between object motion and exerted torque during manipulation of freely pivoting or unstable objects. In the present work, we studied an object manipulation task involving no arm movement to determine how subjects internally represent the force-motion relationship of an object during a skilled manipulation task. Human subjects learned to balance a simulated inverted pendulum. The simulation was controlled by pressing on a fixed force sensor, and applied forces resulted in motion of the simulated pendulum on a computer screen according to its equation of motion. Each subject initially learned the task in one arm posture and was tested 1 d later in a new arm posture. In one test condition, the effects of arm torque were matched to the original task, and in the other test condition, the simulation was unchanged. The pattern of skill transfer to different arm postures suggested that subjects had learned joint torque responses rather than a general model of the object interface forces. A second experiment showed that the advantage of training with matched arm torques was object specific, because torque-matched training on a tracking task involving similar forces was not a substitute for training in the balancing task.

Adult↗

PubMed2003

Evidence for a specific internal representation of motion-force relationships during object manipulation.

Human subjects learned a tracking task which required them to point at a moving target with the free end of an inverted pendulum object. In order to determine how subjects represented this object internally, we studied learning interference between variants of this task in which the pendulum object had either stable or unstable dynamics. Using a novel method, agreement between possible internal representations of the two tasks was estimated by analysis of the motion-to-torque relationships experienced by each subject as they manipulated each object. It was possible to predict retention of the primary task on day 2 from our measure of agreement between primary and interfering tasks on day 1. This result suggests that the subjects learned the correct torque patterns to use to produce specific desired patterns of motion as they learned the balancing task. Surprisingly, the analyses indicate that retention was not impaired when similar motions of the two objects required retrieval of incompatible torque responses, but retention was impaired when similar patterns of motion in the two tasks required similar patterns of applied torque. These findings can be accounted for by a simple model of how multiple similar torque responses are selected and retrieved from memory when responses are freely chosen.

Adult↗

PubMed2002

Manipulating objects with internal degrees of freedom: evidence for model-based control.

There is substantial evidence that humans possess an accurate and adaptable internal model of the dynamics of their arm that is utilized by the nervous system for controlling arm movements. However, it is not known if such model-based strategies are used for controlling dynamical systems outside the body. The need to predict events in the external world is not restricted to the execution of reaching movements or to the handling of rigid tools. Model-based control may also be critical for performing functional tasks with non-rigid objects such as stabilizing a cup of coffee. The present study investigated the strategies used by humans to control simple mass-spring objects. Subjects made straight line reaching movements to a target while interacting with a robotic manipulandum that simulated the dynamics of a one-dimensional mass on a spring. After learning, neither hand nor object kinematics returned to those of free reaching, suggesting that this task was not learned as a perturbation of free reaching. Although there are control strategies (such as slowing the movement of the hand) that would require little or no knowledge of object dynamics, subjects did not adopt these strategies. Instead, they tailored their motor commands to the particular object being manipulated. When object parameters were unexpectedly altered in a way that required no changes in kinematics to successfully complete the task, subjects nonetheless exhibited substantial kinematic deviations. These deviations were consistent with those predicted by a model of the arm-plus-object system driven by a low-impedance controller that incorporated an explicit inverse model of arm-plus-object dynamics. The observed behavior could not be reproduced by a controller that relied on modulating hand impedance alone with no inverse model. These results were therefore consistent with the hypothesis that subjects learn to control the kinematics of manipulated objects by forming an internal model that specified the forces to be exerted by the hand on the object to induce the desired motion of that object.

Adaptation, Physiological↗