Bistable dynamics of control activation in human intermittent control

TL;DR

This paper models human intermittent control in stick balancing as a noise-driven activation process using a double-well potential, capturing core experimental properties and offering insights into control activation mechanisms.

Contribution

It introduces a novel double-well potential model for human control activation, emphasizing noise-driven dynamics over threshold-based approaches.

Findings

Model replicates key experimental control activation features

Double-well potential effectively describes control switching behavior

Suggests broader applicability to complex control processes

Abstract

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 so doing, 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.