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.
Description
Proceedings of the 2004 Winter Simulation Conference, R .G. Ingalls, M. D. Rossetti, J. S. Smith, and B. A. Peters, eds.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.Collections
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