

Type of Document Dissertation Author Orrell, Dean H. URN etd-1849161739741061 Title Performance Assessment Methodology: Task dependent Evaluation of Display Systems Degree PhD Department Industrial and Systems Engineering Advisory Committee
Advisor Name Title Burton, John K. Denton, Robert E. Jr. Koelling, Charles Patrick Beaton, Robert J. Committee Co-Chair Kemmerling, Paul T. Jr. Committee Co-Chair Keywords
- none
Date of Defense 1997-02-27 Availability unrestricted Abstract
As the focus of this research, a new
methodology -- human Performance
Assessment Methodology (PAM), is
introduced. PAM provides a quantitative
basis for evaluating display image quality
based on the visual events that occur in a
task. The PAM approach identifies the
visual events, decisions, and actions for a
display system. To support PAM, a
theoretical model, the Model of Visual
Events (MOVE), is proposed for describing
the relationship between visual events,
decisions, and actions. MOVE describes
four categories of perceptual decisions (i.e.,
detect, identify, discriminate, and evaluate)
associated with visual events. Formal
efficiency metrics are introduced in PAM to
describe performance at the visual event,
task, and network levels. Using PAM, an
efficiency model was created for one visual
display parameter (i.e., luminance), one
decision type (i.e., detection) and one
dependent variable (i.e., visual angle). Two
experiments were accomplished to examine
the validity of PAM. A two-factor mixed
design was employed for both experiments,
where decision type was varied
between-subjects and visual display
parameter (i.e., luminance or sharpness)
was varied within-subjects. In the first
experiment, luminance was varied across
four levels (3.2, 4.5, 8.6, 16.5 cd/m2) for
two decision types (detection and
identification). In the second experiment,
three levels of sharpness (50% spot width -
0.508, 0.711, 0.864 mm) were combined
factorially with two decision types
(detection and identification). In both
experiments, participants visually 'walked
down a path' and either detected or
identified visual targets presented on the
screen. Time-to-target and subjective
responses were measured for each study.
The results of the first experiment show that
time-to-target and subjective rating
significantly change as a function of
luminance. For the sharpness variable in the
second experiment, a significant difference
was found for time-to-target while
subjective rating was non-significant. In both
studies, participants detected visual targets
quickly, but required more time to identify
targets. Using the PAM, functional
relationships for luminance and sharpness
were determined for detection and
identification decisions. When detection
data from the current study were contrasted
with previous detection data, general
agreement was found between the data sets.
This research defines PAM and shows its
utility for modeling the functional
relationships among visual parameters.
Further research is needed to validate and
refine the PAM approach.
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