Every day, humans interact with objects that challenge their ability to control movement stability, such as carrying a cup of hot coffee. For patients with movement disorders, such as those associated with basal ganglia or cerebellar disease, such tasks can significantly impact their ability to perform everyday tasks. However, surprisingly little is known about how the central nervous system (CNS) controls movement when interacting with dynamically complex objects. The CNS uses internal representations of object mechanical properties to manipulate simple rigid objects, and can also learn internal models of complex external force fields for making reaching movements. It was hypothesized that subjects learn similar internal representations of the dynamical properties of complex objects. Preliminary experiments from our lab indicate that healthy subjects adapt their movements to maintain stability of non-rigid objects during a goal-directed reaching task. The proposed research will first endeavor to determine if this adaptation occurs as the result of the development of an internal model of object dynamics, and second, to characterize those aspects of adaptation that are specifically related to maintaining movement stability when manipulating complex objects. Experiments will be conducted using a robotic manipulandum that allows the intrinsic dynamical properties of complex "virtual objects" to be precisely defined. In the first experiment, subjects will be trained to make point-to-point reaching movements while manipulating a dynamically complex virtual object. A comparison of figural errors (which quantify differences in kinematic shape profiles) between movements made with dynamic and rigid objects will be used to determine if subjects are learning and internal model of the object dynamics, or are simply increasing overall limb stiffness or memorizing the specific patterns of forces imposed by the manipulandum. In the second experiment, subjects will make continuous rhythmic movements with the dynamic object. Methods from nonlinear dynamics will be used to quantify movement dimensionality (correlation dimensions) and the sensitivity of the neuromuscular control system to internally-generated local perturbations (Lyapunov exponents). It is hypothesized that the dimensionality of movement is determined by the intrinsic mechanics of the arm+object, and will therefore not change significantly as a function of training, but that the central nervous system adapts its control strategy to make movements less sensitive to local perturbations. It is anticipated that the results of these experiments will lead to a better understanding of the neuromuscular control processes underlying object manipulation and will eventually help provide clearer direction for developing functionally realistic virtual interactions for motor rehabilitation.