I__. - International Military Testing Association

which is a check oE logical necessity, (b) qeneral. which compares est i.mates
with known reqularities, and (c) specific, which compares user input with
library validities. In general, an implicit correlation between aptitude and
the performance dimension on which “proficiency” is defined can be calculated
at every level of training. Also, the implicit correlations should decrease
with training, and the IsoperEormance curves shou Id be decreasing and
negatively accelerated. The results of these checks are repor ted and
explained to the user, together with suggestions as to how the estimate might
be modified to coincide with known regularities and ranges. The fourth phase,
output, is simply the computer output Erom the preceding phases.
AN APPLICATION
The Isoperformance methodology can be applied to numerous human f’actors
areas. Here, we will use as an exemplar freedom Erom simulator sickness in
ground-based flight trainers. Motion sickness is a common problem in the
military, particularly in testing and simulation devices. Virtually everyone
with intact organs of equilibrium is susceptible to one form or another, but
some people get sick all the time while others are virtually immune. However,
we know that practice usually results in adaptation to motion sickness, and
some specific equipment configurations are more conducive to adaptation than
others (e.g., .2Hz).
An example of the approach for applying IsoperEormance to simulator
sickness is as follows:
(1) Obtain a large data base with simulator sickness incidence,
(2) Determine the relationship for each variable,
(3) Isolate variables which are causal,
(4) Select acceptable Isoperformance levels,
(5) Calculate Isoperformance curves using two continuous causal variables
as X/Y and one dichotomous causal variables as comparison,
Afterwards, it is possible to put cost values on the outcomes and determine
trade-offs Erom which decisions can be made.
Therefore, we took from our large data base (N > 1000) of simulator
sickness a series of correlational relationships. We cast them into a
multiple regression equation and obtained the beta weights for such continuous
variables as length of hop, whether visuals are on/off, field of view, usual
state of fitness, etc. using the continuous variables, plus the dichotomous
fit/unfit dimensions, we created Figure 4. Note that a four and one-half hour
hop using a 305-degree field of view for a pilot who was fit would have the
same simulator sickness score (110) as a pilot who had been ill and flew a
two-hour hop with a 195 degree field-of-view.
423