Manipulation of complex objects or tool use is a hallmark of many activities of daily living, but neural control of manual dexterity is still little understood. Even the seemingly simple task of transporting a cup of coffee without spilling creates complex interaction forces that humans need to predict, preempt, and compensate for. Prediction of such complex nonlinear dynamics based on an internal model appears daunting. Hence, this research tests the hypothesis that humans learn strategies that make the interactions predictable. The task of carrying a cup of coffee is modeled with a cart-and-pendulum system that is rendered in a virtual environment and subjects interact with the virtual cup via a robotic manipulandum. To gain insight into human control strategies, this proposal develops three new analysis avenues based on classical linear analysis, information theory, and nonlinear dynamics that operationalize predictability for quantitative theory-based assessment.
Aim 1 applies classical frequency response analysis and tests the hypothesis that humans tune into resonance modes as they not only require lower forces, but also more predictable due to lower signal-dependent noise. Three experiments examine transient and steady-state performance with the linear and nonlinear task model.
Aim 2 examines tasks with redundancy that offers a manifold of solutions. Predictability is operationalized by the mutual information between the applied force and object dynamics. Three experiments test whether subjects choose those strategies with the highest mutual information.
Aim 3 applies contraction analysis, a theoretical framework that examines convergence, or stability, in the state space of the dynamical system. Two experiments examine whether subjects learn solutions that maximize contraction of their trajectories, especially when confronted with perturbations. As manual dexterity is compromised in many individuals with neurological disorders, the experimental paradigm and its analyses promise to become a useful platform to gain insights into neurological diseases, such as dystonia, multiple sclerosis, including aging.
Manipulation of complex objects such as transporting a cup of coffee without spilling creates complex interaction forces that humans need to predict, preempt, and compensate for. Using a virtual experimental set-up that simulates the task of 'carrying a cup of coffee' and novel analysis approaches, this research aims to show that humans learn control strategies that make object dynamics predictable. This research will help gain insights into many neurological diseases that compromise manual dexterity, such as dystonia, multiple sclerosis, and apraxia, including aging.
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