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nervous system combine in the perception of angular
movement have been treated in several research summaries
(refs. 28–31). Subtle differences exist among the model
structures reviewed. Significant differences, sometimes by
several orders of magnitude, exist among measured
numerical values in the respective structures. This
appendix discusses factors that are important in pilotvehicle
dynamics modeling and in understanding human
perception of motion in flight simulation.
A model for angular velocity sensation is shown in
figure A1. The semicircular canals are roughly orthogonal
to each other, and each behaves like an overdamped
torsional pendulum (ref. 32). The output of each block is
the deflection of the canal’s cupula, and when it reaches a
threshold deflection, a sensation develops. Van Egmond
et al. (ref. 32) determined the time constants for yaw to be
0.1 sec and 10 sec. The low-frequency poles shown in
figure A1 are those determined in a later study by Jones
et al., who assumed that the torsional pendulum dynamics
developed a sensation of angular velocity (ref. 33). The
dynamical differences among axes do not have a satisfying
physical explanation and may lie at the behavioral level as
suggested by Zacharias (ref. 31). The delay from the
central nervous system was included by Levison et al.
(ref. 35) for the yaw axis and was carried over to the other
axes. The thresholds shown are based on a summary given
by Zacharias (ref. 31).
Differences among individuals have been noted in both the
dynamics and the thresholds, and it is known that the
thresholds can vary up to an order of magnitude, depending
on whether a subject has to perform a task (such as flying
a simulator) (ref. 36).
While the semicircular canals act as effective rate gyros,
the utricles in the inner ear act as effective linear
accelerometers. Peters (ref. 28) provides a block diagram
of the sensing path for the utricles, which is shown in
figure A2. The transfer function was developed by Meiry
(ref. 22) with a subject experiencing longitudinal motion
only. No dynamic data have been determined in the
vertical or lateral axes. The dynamics of the utricles act as
a bandpass filter between 0.1 and 1.5 rad/sec. The cutoff
frequency of 1.5 rad/sec suggests that high-frequency
accelerations must be sensed by the tactile mechanisms
in the body and not by the vestibular system. A wide
variance exists in the literature for the translational
specific force sensing threshold, which is dmin in
figure A2. Peters reviewed thresholds from seven sources
and found that they ranged from 0.002 and 0.023 g’s
(ref. 28).
In addition to these vestibular models, nonvestibular
motion sensing plays a prominent role in motion
perception. Gum states that “For man in flight the
component of the vestibular apparatus, semicircular canals
and otolith, do not seem to be very reliable or useful
force- and motion-sensing mechanisms” (ref. 30). He
summarizes nonvestibular models, with a model of the
control of lateral head motion shown in figure A3. Here,
the head is essentially an inverted pendulum with respect
to the pilot’s body that is strapped into a moving cockpit.
Taps of the physiological feedback system that regulates
the head position serve as an effective motion cueing
source. The closed-loop dynamics of the head-control
model have a real-axis pole at 3 rad/sec with the rest of the
poles at frequencies higher than 10 rad/sec. Thus, the
bandwidth of the head-positioning control is twice that of
the vestibular system.
The final sensing model covers body pressure sensing;
very few quantitative data are available to describe its
dynamic response. The body-pressure model shown in
figure A4 is from Gum (ref. 30); it has a natural frequency
of 34 rad/sec. This bandwidth would make the body’s
pressure response dynamics the highest of all of its
motion-sensing capabilities. The 1-sec time-constant
high-pass filter in the model is due to the adaptation effect
wherein the receptors in the skin lose their sensitivity to
sustained acceleration.
These models represent the latest research findings in the
field of motion sensing, but they are incomplete and have
limitations. Zacharias points out that there have to be
studies to develop an integrated cueing model (ref. 31).
To summarize, substantial work in human sensory
modeling has provided useful, but incomplete, information
for predicting motion platform effects on pilotvehicle
performance and workload. Rather than use these
detailed sensory models, appendix B illustrates that some
　
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