Neurocognitive Engineering for Systems’ Development - Neurotechnology in National Security and Defense - page 42

brain, unlike performance measures or rating scales. And while current technolo-
gies are still fairly cumbersome to use (e.g., requiring significant setup time and the
application of electrolytic gels), technological advances hold the promise of nearly
noninvasive, zero-preparation EEG recording (Lin et al. 2008; Matthews et al. 2007;
McDowell et al. 2013; Sellers et al. 2009).
Progress in computational power and data analytic techniques has also enabled
the development and application of novel signal analysis and decomposition methods
(Gramann et al. 2011; Jung et al. 2001; Makeig et al. 1996), as well as advanced data
mining techniques (Garrett et al. 2003) for data processing and knowledge discovery in
highly multidimensional data in ways that have clearly surpassed our previous capabili-
ties. These advances have great potential to improve EEG technology, enhancing its spa-
tial resolution relative to the current state-of-the-art in neuroimaging technologies (e.g.,
fMRI) and moving neuroscience-based cognitive assessment into the operational realm.
While further technological advances and methodological developments are still
needed, the current tools of neuroscience, when integrated with complementary
approaches of more traditional methods, can provide more complete characterizations
and understandings of cognitive (or somewhat more specifically, neurocognitive) per-
formance in operational environments (for recent discussions, see Gordon et al. 2012).
A recently developed system that implements this “multi-aspect” approach was
described in Doty et al. (2013). Though that implementation was optimized for the
study of stress, the advances embodied in the system could be applied to study other
naturally occurring phenomena. The multiaspect approach will be critical not only
for systems engineers who are developing systems to meet the challenges of the
current and future security environment, but also for cognitive systems engineers
who aim to facilitate performance by focusing on the “thinking” aspects within
such socio-technical systems (McDowell et al. 2009). In the following two sections,
we discuss an important issue—individual differences—in which a neurocognitive
engineering approach may have significant potential for enhancing systems design.
DIFFERENCES IN OPERATOR CAPABILITIES
One of the most common, yet more difficult systems engineering issues is the need to
account for the individual difference in operator capabilities. Cognitive research has
revealed that people not only differ in classical categories of mental function, such as
intelligence, skill set, or relating to past experience, but they also differ on a more fun-
damental level in how they think (i.e., cognitive styles, abilities, and strategies). These
differences arise from many factors, including inherent characteristics of the operators
and how operators are affected by stressors, such as emotionality and fatigue. A growing
body of evidence suggests that individual differences in cognition, behavior, and perfor-
mance of skilled tasks are rooted, at least to some extent, in differences in neural func-
tion and/or structure (National Research Council 2009). This has been supported by the
association of genetic markers with variability in brain size, shape, and regional struc-
ture (Tisserand et al. 2004); elucidation of differences in nervous system connectivity
that relate to different patterns of cognitive activity (Baird et al. 2005; Ben-Shachar
et al. 2007); and demonstrating variability in individual patterns of brain activity
(Chuah et al. 2006; Miller and van Horn 2007; Miller et al. 2002). These findings