AAWSAP DIRD, Cockpits in the Era of Breakthrough Flight, November 2010

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Chapter 2: Human-Machine Interface Lessons
Over recent decades, substantial improvements have been made to human-machine
interfaces. 15 Most of this progress relies on better accommodating the norms and limits
of human perception-lessons that do not change even when vehicle characteristics
change. These lessons are reviewed in this chapter and then applied in the conceptual
design offered in the third chapter. Examples of such characteristics include reaction
times, tunnel vision under stress, instinctual association with position, interpretations of
displayed colors, and lessons learned from interactions with display and control
technologies.
Recent progress on augmented rea lity displays, 16 voice control, 17 gesture-based
computer inputs, 18 and brain-machine interfaces (BMis) 19 are also considered. In this
study of far-future possibilities, these technologies are assumed to have reached full
maturity. Instead of going into the details of their status, only their implications will be
addressed here.
One exception was made when considering emerging technologies-specifically the
notion of modifying humans for breakthrough flight. This exception includes brain
implants for BMis and reengineering humans (transhumanism) to adapt to new
requirements. 20 Rather than requiring humans to be reengineered for breakthrough
flight, this study focuses on adapting the cockpit to address natural human
characteristics. This forces attention on the cockpit design requirements.
Numerous references about human factors were consulted, focusing on those details
most relevant to this study. Since similar assertions were echoed in many of these
references, it is not always clear how to trace a given assertion to a specific reference.
Instead, an annotated bibliography is included at the end of this report that has short
descriptions of each reference.
HUMAN PERCEPTION NORMS
Physical Object Analogs
Human interpretation mechanisms are rooted in the paradigm of a physical
environment. Mimicking a physical environment in a display and control system
enhances comprehension and allows features to be recognized with less effort than
when translating dials, bar graphs, or alphanumeric displays. 21 This not only pertains to
perceptions of motion, but also to the comprehension of a vehicle's operation:
understanding its limits, supplies of consumables, malfunction modes, and other
parameters.
Accordingly, humans naturally remember where to look for a particular piece of
information or what direction to flip a particular switch. Humans are also adept at
subconsciously applying models of social behavior and causal events.
Hierarchies,
similar to societal organizations, offer a natural model with which to categorize systems
and subsystems. Causal relations (cause leads effect) match well with procedures and
operational flow diagrams. For more abstract concepts and complex data sets, a
combination of hierarchical and causal relations can be used, typically taking the form
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