2014/02/13 by Arkady Zgonnikov, Ihor Lubashevsky, Zgonnikov, Arkady +7
Health Professions · Neuroscience · Psychology · #Adaptation and Self-Organizing Systems (nlin.AO) #Biological Physics (physics.bio-ph) #FOS: Biological sciences #FOS: Electrical engineering #FOS: Physical sciences #Human-Automation Interaction and Safety #Motor Control and Adaptation #Neurons and Cognition (q-bio.NC) #Occupational Health and Safety Research #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1402.3022
openalex publication_date 2014/02/13 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/01
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.