Human Control of Interactions with Objects – Variability, Stability and Predictability

How do humans control their actions and interactions with the physical world? How do we learn to throw a ball or drink a glass of wine without spilling? Compared to robots human dexterity remains astonishing, especially as slow neural transmission and high levels of noise seem to plague the biological system. What are human control strategies that skillfully navigate, overcome, and even exploit these disadvantages? To gain insight we propose an approach that centers on how task dynamics constrain and enable (inter-)actions. Agnostic about details of the controller, we start with a physical model of the task that permits full understanding of the solution space. Rendering the task in a virtual environment, we examine how humans learn solutions that meet complex task demands. Central to numerous skills is redundancy that allows exploration and exploitation of subsets of solutions. We hypothesize that humans seek solutions that are stable to perturbations to make their intrinsic noise matter less. With fewer corrections necessary, the system is less afflicted by long delays in the feedback loop. Three experimental paradigms exemplify our approach: throwing a ball to a target, rhythmic bouncing of a ball, and carrying a complex object. For the throwing task, results show that actors are sensitive to the error-tolerance afforded by the task. In rhythmic ball bouncing, subjects exploit the dynamic stability of the paddle-ball system. When manipulating a “glass of wine”, subjects learn strategies that make the hand-object interactions more predictable. These findings set the stage for developing propositions about the controller: We posit that complex actions are generated with dynamic primitives, modules with attractor stability that are less sensitive to delays and noise in the neuro-mechanical system.