Predictability in complex object control - PROJECT SUMMARY Manipulation of complex objects or tool use is a hallmark of daily living, and loss of manual dexterity due to motor impairments lead to loss of independence. Manipulating objects is particularly challenging when the object has internal dynamics that is not directly controlled. Even the seemingly simple task of transporting a cup of coffee has intrinsic dynamics that humans need to predict, preempt, and compensate for to avoid spilling. Control of such complex nonlinear systems with online error corrections based on precise internal models appears daunting, given the slow neural processes and the ubiquitous noise in the sensorimotor system. Hence, this research tests the hypothesis that humans learn to simplify the object interactions, i.e., 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 a task-dynamic approach that affords principled hypothesis-testing by parsing the complex dynamics into execution and result variables, with minimal assumptions about the human controller. Eight experiments test the overall hypothesis that humans seek solutions that are predictable, by correlating hand-object motions, and making the behavior stable and tolerant to error and risk to obviate error corrections and prevent failure. Aim-1 tests control of internal dynamics in linear movements and examines how humans choose initial conditions to mitigate perturbations, how they preempt undesired ball oscillations, how they exploit intermittent contact to develop a stable rhythm, and how they modify the object properties to facilitate stable contact behavior. To examine learning, Aim-2 scales up the dimensionality of the task by introducing more real-life planar cup movements, which creates an exponential increase in complexity. Four experiments test task goals that introduce new dynamic challenges, such as combination of rhythmic and discrete movements, complex ball dynamics when changing movement directions, adaptation and modification of object properties, all to show how humans either exploit or override internal dynamics to achieve predictability. Aim-3 introduces a real version of the task with a custom-designed device, the MAGIC Table. Following a comparison of the real and virtual set-ups, the MAGIC Table is used to leverage the theoretical framework to create novel sensitive metrics to quantify motor function for clinical applications. Specifically, we assess severity and recovery of motor impairment in a cohort of patients after stroke. As manual dexterity is compromised in many individuals with neurological disorders, the experimental paradigm and its quantitative analyses promise to become a useful platform to gain insights into neurological diseases.
