  Understanding how humans control unstable systems is central to many research
problems, with applications ranging from quiet standing to aircraft landing.
Increasingly much evidence appears in favor of event-driven control hypothesis:
human operators only start actively controlling the system when the discrepancy
between the current and desired system states becomes large enough. The
event-driven models based on the concept of threshold can explain many features
of the experimentally observed dynamics. However, much still remains unclear
about the dynamics of human-controlled systems, which likely indicates that
humans employ more intricate control mechanisms. The present paper argues that
control activation in humans may be not threshold-driven, but instead
intrinsically stochastic, noise-driven. Specifically, we suggest that control
activation stems from stochastic interplay between the operator's need to keep
the controlled system near the goal state on one hand and the tendency to
postpone interrupting the system dynamics on the other hand. We propose a model
capturing this interplay and show that it matches the experimental data on
human balancing of virtual overdamped stick. Our results illuminate that the
noise-driven activation mechanism plays a crucial role at least in the
considered task, and, hypothetically, in a broad range of human-controlled
processes.
