Vigilance Performance Modeled As A Complex Adaptive System With Listener Event Graph Objects (LEGOs)

Abstract

There has been an increasing need to incorporate human performance in simulation models. Situations in which human performance is subject to degradation over time, such as vigilance tasks, are not represented. This article describes a computational model for vigilance performance embedded in a new cognitive framework that utilizes recent advances in system neuroscience, evolutionary psychology and complexity theory. The Reduced Human Performance Model (RHPM) captures human errors in monitoring tasks to a greater degree than previous attempts. RHPM is implemented as a discrete event simulation using Listener Event Graph Objects (LEGOs). The model captures leading vigilance theories and can be used as a tool to improve existing vigilance theories and to improve current monitoring procedures minimizing errors that could lead to catastrophic outcomes.