Biomedical Engineering Reference
In-Depth Information
in turn, influences the continuing situation assessment process. The holistic state of
knowledge represented by the three levels of agents is termed situation awareness.
This modular approach is attractive because it extends inductively to the situation
awareness of a group of interactive decision makers. For example, Endsley (1995)
also defined Team Situation Awareness as the degree to which each member of a
team possesses the situation awareness that is necessary for their responsibilities.
Since the situation awareness of each team member can be envisioned as collec-
tion of three simple, smaller autonomous processes for situation assessment, the net
construct parallels Marvin Minsky's (1986) metaphorical description of an artificial
intelligence “society of mind.” The quality of team coordination can be assessed by
the quality of the situation awareness of team members with shared responsibilities.
Conversely, potential vulnerabilities or weak links are produced by a team member
lacking situation awareness for one element of a responsibility area. In this sense,
the cognitive architecture in Figure 6.1a is one of the rational agents that serves as a
basic building block of individual and group situation awareness.
Much thought and effort has been directed at developing operational situation
assessment and intelligent data fusion processes that can approach an ideal of omni-
science for command and control applications However, public health and medical
operations during high consequence events are necessarily undertaken in an uncer-
tain environment where it is unrealistic to expect global situation awareness. Stated
simply, the agents (e.g., the victims, the worried well, the general public, and the
response community) all act in the absence of a complete forensic picture (Alexander
and Klein 2006). Therefore, one is left with a default approach: we regard an opera-
tional scenario pragmatically as a collection of quasi-independent agents, with each
agent acting upon situation assessments that reflect a personal frame of reference.
As a result, the fidelity of predictive modeling of the processes and consequences
of situation assessment by the agents becomes an essential factor in effective opera-
tional medicine.
Figure 6.1b shows a prototype for a neurotechnology-based representation of
an agent that performs situation assessment of its internal state of health. It is an
extension of a framework that is being developed to understand the bases for comor-
bid aspects of balance disorders, migraine, and anxiety disorders. This heuristic
schema distinguishes three basic underlying brain process classes: sensorimotor
processing, interoception, and cognitive processing. It is important to note that
each component is a “black box” that represents more complex computations and
interrelationships for functions that include threat assessment (Staab et al. 2013).
Sensorimotor processing includes afferent activity from the externally and inter-
nally directed neuronal sensors (e.g., peripheral mechanoreceptors, chemorecep-
tors, and photoreceptors) and circuitry that mediates their conversion into overt
responses associated with perception and action (somatic, endocrine, and auto-
nomic responses). The interoception and cognitive processing components are
modified from schemata that have been proposed in the area of functional somatic
syndromes and medically unexplained physical symptoms (Barsky and Boras 1999;
Barsky et al. 2002; Brown 2004; Mayou et al. 2005). Interoception is used here
in the broad sense proposed by Cameron (2002) as any effect of internal sensa-
tions on molar organic activity, even in the absence of awareness. In the case of
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