Double-well dynamics of noise-driven control activation in human intermittent control: the case of stick balancing.
When facing a task of balancing a dynamic system near an unstable equilibrium, humans often adopt intermittent control strategy: Instead of continuously controlling the system, they repeatedly switch the control on and off. Paradigmatic example of such a task is stick balancing. Despite the simplicity of the task itself, the complexity of human intermittent control dynamics in stick balancing still puzzles researchers in motor control. Here we attempt to model one of the key mechanisms of human intermittent control, control activation, using as an example the task of overdamped stick balancing. In doing so, we focus on the concept of noise-driven activation, a more general alternative to the conventional threshold-driven activation. We describe control activation as a random walk in an energy potential, which changes in response to the state of the controlled system. By way of numerical simulations, we show that the developed model captures the core properties of human control activation observed previously in the experiments on overdamped stick balancing. Our results demonstrate that the double-well potential model provides tractable mathematical description of human control activation at least in the considered task and suggest that the adopted approach can potentially aid in understanding human intermittent control in more complex processes.
University of Aizu, Ikki-machi Tsuruga, Aizuwakamatsu city, Fukushima, 965-8580, Japan. arkady@u-aizu.ac.jp.
Arkady
A
Zgonnikov
University of Aizu, Ikki-machi Tsuruga, Aizuwakamatsu city, Fukushima, 965-8580, Japan. i-lubash@u-aizu.ac.jp.
Ihor
I
Lubashevsky
D005260:Female; D006801:Humans; D008297:Male; D008962:Models, Theoretical; D009068:Movement / Q000502:physiology*; D017711:Nonlinear Dynamics; D004856:Postural Balance / Q000502:physiology*; D011597:Psychomotor Performance / Q000502:physiology*
Bistable dynamics; Intermittent motor control; Stick balancing; Stochastic modeling