1999 Systems Engineering Capstone Conference • University of Virginia
ERICA, INC. CAPSTONE 1998-1999
Student team: Dayna Balcome, Peter K. Bokach, Justin Petersen
Faculty Advisors: Michel A. King, Department of Systems Engineering
Client Advisors: Professor Thomas E. Hutchinson, Chris Lankford
ERICA, Inc.
P.O. Box 615
Ivy, Virginia 22945
E-mail: ericainc@esinet.net
KEYWORDS: Eye, Eye gaze, Lie detection,
Advertising, Database design
ABSTRACT
The main goal of this year's Capstone team was to
explore potential new markets for the ERICA (EyeGaze Response Interface Computer Aid) system as well
as continue to serve its current clients. Dayna Balcome
studied ERICA’s potential as a non-invasive deception
detection device. She tested the system using the
Control Question Test interrogation procedure on 28
subjects while the system measured the subjects’
pupillary size over time. Pupillary response was scored
using the techniques described by Lubow and Fein
(1996), resulting in correct classification of 75% of the
subjects. Peter Bokach’s research explored the potential
use of ERICA in the advertising industry. The purpose
of his project was to find a correlation between dwell
times in Trademark LookZones and recognition
supported by Starch Readership Reports. Justin
Petersen designed and developed a database system to
handle the saving and retrieval of data for the ERICA
application, GazeTracker. This system has decreased
the workload required of the ERICA programmers for
future versions of the application thereby increasing
development efficiency. Together, these projects
demonstrated new and better ways in which the
versatile ERICA system can be utilized.
INTRODUCTION TO ERICA, INC.
The ERICA project began in 1984 under the
direction of Dr. Thomas Hutchinson, the Calcott
Professor of Systems Engineering at the University of
Virginia. Dr. Hutchinson sought to design a system that
would enable disabled individuals to use computer
systems in order to communicate effectively. ERICA
allows users to control a computer by emulating mouse
functions with their eyes. Over the past 15 years, the
project has grown in size and scope, and the project was
made into a company, ERICA Inc., in 1994. Dr.
Hutchinson is the President of this corporation and
Chris Lankford, a graduate student at the University of
Virginia, is Vice-President.
In recent years, ERICA, Inc. has been exploring the
uses of the ERICA system in applications other than as
a communication aid for the physically handicapped.
This year’s ERICA Capstone team, under the guidance
of Michel King, the technical advisor, aimed to develop
a foundation of experimental data that would support
these alternative uses for the ERICA system and sought
to develop a database that would increase development
efficiency for the ERICA programmers.
THE ERICA SYSTEM
The ERICA system, using its software and
hardware capabilities, tracks the user’s eye and
achieves the function of an eye-controlled mouse. The
system tracks movement of the eye by monitoring
optical features which may be translated to a gazepoint,
or a point of regard. This represents the location of the
user’s gaze on the screen. In this way, the input from
the user's eye allows the ERICA system to execute an
unlimited number of functions. In particular, the
ERICA system is configured to record the point of
regard and ocular characteristics, such as pupil area, as
a function of time and position on the computer display.
The ERICA system can also grant complete control
over the computer using only the eye (“A User’s Guide
to ERICA” 1999).
The system hardware, through the use of a light
emitting diode (LED) reflects harmless infrared light
off of a mirror positioned towards the user’s eye.
Figure 1 shows the ERICA hardware mounted beneath
the computer monitor.
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Erica, Inc. Capstone 1998-1999
Figure 1 The Camera projects infrared light onto the
mirror which reflects it into the user’s eye.
The eye absorbs some of the infrared light and re-emits
some from the retina. This phenomenon illuminates the
pupil, which is then called the bright-eye. The position
of the bright eye moves in the camera image field,
following the motion of the eye. The corneal surface
reflects an intense point of light, which remains
stationary as the pupil position changes with the user’s
gaze on the screen. This point, called the glint, serves as
a reference for determining the user’s gaze point or
focal point during the program he or she uses. Figure 2
shows how this harmless infrared light illuminates the
eye.
Glint
Bright Eye
Figure 2 Image of the eye with the Glint and Bright Eye.
The vector distance between the center of the pupil and
the center of the glint provides necessary information
for tracking the user’s gaze points on the screen and
changes in pupil diameter. The ERICA software then
analyzes these images to determine gaze position. In
this way, disabled individuals who are physically
unable to operate a mouse or the keyboard, can use a
computer by simply focusing on the screen (“A User’s
Guide to ERICA” 1999). In addition, the ERICA
system is completely non-invasive, that is, it requires no
physical attachment to the subject.
ERICA’s capability to measure and track changes
in eye position and pupil size over time implies that the
system may have other applications outside of its
benefits for the physically disabled.
DECEPTION DETECTION
Problem Definition
This project was based on past research that has
determined that pupil dilation can be a physiological
100
indicator of fear or anxiety (Ford 1996), and pupil
dilation increases with the workload imposed on an
individual’s mental processes (Kahneman and Beatty
1966). It was hypothesized that by tracking an
individual’s pupillary response in a lie detection
experiment, the ERICA system can detect increases or
decreases in stress levels or mental processing. Because
the ERICA system is physically noninvasive, it can
minimize undue stress in the test subject. In addition,
according to Ford (1996), the pupils are governed by
the autonomic nervous system and thus are out of the
liar’s control. Therefore, it would be very difficult for a
subject to devise countermeasures to this system.
Experiment
Regardless of the interrogation format that is used,
the theory behind all lie detection techniques is the
same, namely, the physiological response occurring
with deception is different from the physiological
response to telling the truth. In testing ERICA as a lie
detector, the Control Question Test (CQT) format was
used as it is the technique most commonly used when
investigating specific issues. It centers on two types of
yes/no questions: relevant and irrelevant. Working with
Sandra Williams, a fourth year Electrical Engineering
student, we modeled our experiment after that described
by Bradley, MacLaren, and Black (1996). In this case,
one group of the test subjects committed a mock crime,
others were informed of the relevant facts about the
crime, and the third group remained innocent (Bradley
1996). Fifteen male and fifteen female undergraduate
students at the University of Virginia were used as
subjects, and five female and five male subjects were
assigned to each of the three groups. Each subject
signed a consent form and was tested individually. Two
technicians were present during the testing process; one
acted as the interrogator while the other was the
assistant who worked with the subject to ensure his or
her understanding of the proceedings.
Each subject was given instructions as to what his
or her condition was, i.e. innocent, innocent informed,
or guilty, and the instructions described the actions they
were to carry out as part of the mock crime. Guilty
subjects were told to go into Office 101f in Olsson Hall
and steal $7.00 out of a desk. The innocent-informed
were merely told of the crime and that they were to be
interrogated, and the innocent were only told that a
crime had been committed and that they were going to
be questioned. A blank gray slide was presented to the
subject to stare at while he or she answered the
questions, and the ERICA System monitored the test
subjects’ pupillary response during each question.
Participants were told they would receive $10 if they
were found innocent.
1999 Systems Engineering Capstone Conference • University of Virginia
The entire set of ten questions was presented 3
times to each subject during the interrogation. There
were four relevant questions paired with a similar but
irrelevant question, and the order of the irrelevant and
relevant questions in each pair was randomized each
round. Additionally, two irrelevant questions were
posed regarding the subjects’ first and last names. An
intro slide as well as a 10 to 20 second break between
rounds was added after testing the first 19 subjects per
the recommendation of a professional polygrapher. The
sampling rate for pupil diameter was approximately 28
samples per second, and pupil size was measured in
pixels. Y pupil diameter is often inaccurate due to eye
rotation. Therefore, only X pupil diameter was used for
analysis.
A simple scoring algorithm (Lubow and Fein 1996)
was applied to the average X pupil diameter and
standard deviation on each slide. Each subject was
awarded two scores from 0 to 6, one score for mean
pupil size and one for standard deviation. Each subject
could earn up to 2 points per round. They received a 2
if the largest pupil diameter (or standard deviation) in
the round occurred on a guilty question, a 1 if the
second largest pupil diameter (or standard deviation)
occurred on a guilty question and a 0 otherwise. Ideally,
innocent or innocent-informed subject should score a 4
or below; Guilty subjects should score 5 or higher.
Results and Conclusions
Using the mean scoring system, the ERICA system
identified 78% of the guilty subjects correctly, 67% of
the innocent-informed correctly, and 80% of the
innocent correctly. Overall, the system correctly
classified 75% of the subjects, while the average
accuracy of current polygraph procedures is only 70%.
Figure 3 shows the results of this algorithm.
Results of Scoring Algorithm
The difference in mean scores is significant at the 98%
confidence level for guilty versus innocent subjects and
at the 82% confidence level for guilty versus innocent
informed, but the difference between innocent and
innocent informed is probably not significant (34%
confidence level). The estimated minimum sample
sizes required for 95% confidence in the observed
differences, if the observed differences are significant,
would be 5 subjects for the guilty vs. innocent
comparison, 9 for guilty vs. innocent informed, and 79
for innocent vs. innocent informed. In this experiment,
after discarding one guilty subject’s data due to testing
error, there were 19 subjects in the guilty vs. innocent
comparison and 19 in the guilty vs. innocent informed
comparison. Thus, the sample sizes were adequate to
support our conclusions concerning the significant
differences between these groups. The 20 subjects in
the innocent vs. innocent informed comparison did not
provide a large enough sample size to assume that the
differences in the observed mean scores are statistically
significant.
Further statistical analysis was based on
normalized X diameters for all subjects. This was
accomplished by dividing each subjects’ X diameter
observations by their total mean X diameter over all
questions. Several possible relationships in the data
were investigated including statistical comparisons of
each question’s mean and standard deviations to every
other question’s data for each subject and statistical
comparisons of overall mean and standard deviation for
all questions and all subjects. However, Figure 4
illustrates the strongest results apparent in the
experimental data. It seems that there is a difference in
the mean pupil response between irrelevant and
relevant questions. The p values for this relevant versus
irrelevant question comparison were 0.050 for the
guilty subjects’ response, 0.009 for the innocent
subjects’ response, and 0.084 for the innocent informed
subjects’ response.
7
6
Score
5
4
3
2
1
G
0
0
5
10
15
Subject #
20
25
30
I
I-I
Figure 3 The scoring algorithm. Guilty subjects should
score 5 or above; innocent and innocent informed should
score 4 or below.
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Erica, Inc. Capstone 1998-1999
Mean Comparison
1 .01
1 .005
Pupil Mean
1
0.995
0.99
0.985
0.98
0.975
0.97
Relevant
G
I
I-I
Irrelevant
Question Type
Figure 4 Mean Comparison by Subject Group of
Normalized Pupil Size for Relevant compared to
Irrelevant Questions
Unfortunately, however, in order to prove that
normalized X diameters are a successful deception
detector, a comparison across subject groups for
relevant questions and irrelevant questions would have
to produce low p values. When these comparisons were
made, the following p values (listed in Table 1 and
Table 2) resulted:
P Value
Guilty
Innocent
Innocent
0.77
*
Inn-Inf
0.80
0.94
Table 1 P-Values for Relevant Question Comparison
P Value
Guilty
Innocent
Innocent
0.76
*
Inn-Inf
0.83
0.99
Table 2 P-Values for Irrelevant Question Comparison
The p values are lower for the comparison between
Guilty and both Innocent and Innocent Informed
subjects for both question types, however they only
indicate an approximate 15-25% confidence level.
Thus, we conclude that a comparison of the mean
normalized X diameter for a subject’s response to
relevant questions versus the mean normalized X
diameter for a subject’s response to irrelevant questions
is not a useful technique for detecting deception. The
comparison of the standard deviation of the normalized
X diameter for a subject’s response to relevant
questions versus the standard deviation of the subject’s
response to irrelevant questions was also not useful.
Recommendations for Future Study
The experiment documented in this report shows
that ERICA is capable of performing as a deception
detection device. However, we believe that further
102
analysis would lend additional insights. For instance,
the manner in which the questions were labeled
irrelevant or relevant could have affected our results.
For this reason, further analysis should include a
comparison of guilty, innocent, and innocent-informed
responses to each question to determine whether some
questions were more useful than others in detecting
deception. As time was a serious constraint in the
analysis of the results, this type of analysis could not be
conducted.
This project not only contributes to the potential of
ERICA, Inc. to compete in the lie detection industry,
but it also contributes a significant amount of research
to the study of pupillometry, an area that is new to all
fields of research. Future ERICA researchers or
Capstone team members may consider testing ERICA’s
capabilities to perform successfully using the another
interrogation format, the General Knowledge Test
(GKT), as Lubow and Fein did in 1996. In addition,
future projects should continue to work with a
representative from the CIA’s Department of Technical
Services in order to project ERICA’s presence as a
marketable deception detection device further into the
lie detection industry.
ADVERTISING
Problem Definition
This project sought to determine the correlation
between dwell times using visual fixations determined
by the ERICA system’s software and recognition
supported by Starch Readership Reports as applied to
print advertisements.
Advertising effectiveness is determined by readers’
abilities to recall print advertisements and aspects of
each advertisement that depict the advertiser’s name,
brand, or company logo. Since companies spend large
and ever-increasing amounts of money to analyze print
advertisements each year, a faster and more costeffective analysis tool would be beneficial to
advertising agencies, advertising research companies,
businesses, and consumers. Therefore, proving that
ERICA is an effective system for predicting the success
or failure of advertisements may have a significant
social, cultural, and economic impact within the United
States and throughout the world. By providing a more
cost-effective and expedient method to analyze print
advertisements, advertisements will reach the general
public sooner and at less cost to companies.
Consequently, consumers may be able to enjoy
products sooner and at lower cost.
1999 Systems Engineering Capstone Conference • University of Virginia
Experiment
To prove the ERICA system is an effective tool for
analyzing advertisements, we compared the data
collected using ERICA with pre-tested data on identical
advertisements. The pre-tested data is supplied by
Starch Readership Reports, an industry standard for
analyzing advertisements. An example of a Starch
Report for Cover Girl is provided in Table 3. In the
table, % Noted means the percentage of readers that
remember previously viewing the advertisement, %
Associated means the percentage of readers that
remember the logo or advertiser’s name, and % Read
Most means the percentage of readers that read most of
the body text.
Advertisement % Noted
Cover Girl
69
% Associated % Read Most
67
47
Table 3 Starch Report Scores
Because our pre-tested data was obtained from
female readers viewing Glamour magazine
advertisements, we tested seventeen female test
subjects viewing twenty advertisements, such as the one
shown in Figure 5.
Figure 5 Thermasilk Advertisement
After observing the correlation between a test
subject’s dwell times in ERICA LookZones and recall
as measured by Starch Readership Reports, we assessed
the effectiveness of ERICA as an analysis tool for the
advertising industry.
Results and Conclusions
There is a great deal of quantitative data that can be
utilized to support the claim that ERICA is an effective
analysis tool. When comparing each test subject
individually with all of the advertisements, analysis
revealed that a strong negative correlation (=-0.855,
p=0.02) exists between the Starch Associated data with
high scores and the Average Time in Trademark
LookZones for all test subjects. Trademark LookZones
refer to LookZones around the logo and advertiser’s
name within an ad.
As the average test subject spends more time
looking at the brand or advertiser’s name, she is less
likely to remember the name of the brand or advertiser.
Several possible explanations for this observation are:
 Some advertisements may already be familiar to
some readers, especially if they currently use that
product. Thus, such readers may decide to spend
their time looking at other aspects of the ad, rather
than looking at a brand name or logo that they are
already familiar with. In such cases an increase in a
reader’s Time in Trademark LookZones might be
correlated with a lower likelihood of her making an
association.
 When an advertisement is unfamiliar to the reader,
she may spend more time on the advertiser’s name
because it is foreign to her. Then, when asked to
recognize the specific brand or name in question,
she may still be sufficiently unfamiliar with that
advertiser’s name that she cannot associate it with
the product.
 Readers may recognize advertisers’ names or
brands that are connected to the theme or general
concept of the entire advertisement. For example,
a reader may spend minimal time reading the
“Wonderbra” label; but, if she spends a great deal
of time looking at the model in lingerie she may
associate the entire picture with the advertiser
Wonderbra.
These suggestions are only some of the possible
explanations as to why the negative correlation exists.
Additional research will be required to determine all of
the contributing factors. Although our experiment does
not provide insight into the causal mechanism, we
conclude that Time in Trademark LookZones, as
measured by ERICA, is proven to be a negative
indicator of advertiser or brand association. When
coupled with additional information concerning causal
factors, this should help ad agencies and ad researchers
predict the successfulness of print advertisements.
Recommendations for Future Study
Our recommendations for follow-on research are:
 Extend our research and analysis of Starch
Readership Reports in order to look for other
significant correlations within the data available.
 Test a larger number of subjects and use different
publications other than Glamour in order to
differentiate the types of advertisements.
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Erica, Inc. Capstone 1998-1999


Contact other advertisement research agencies and
test the ERICA system against their testing
methodologies.
Explore the use of ERICA to analyze television
commercials and quantitatively assess their
effectiveness to sell products or services.
opened and display the user’s direction of gaze as it is
used.
We have provided convincing evidence as to the
effectiveness of ERICA to analyze print
advertisements. Therefore, we hope that additional
time and resources will be expended to fully realize the
potential of ERICA in the advertisement analysis
industry.
INCREASING DEVELOPMENT EFFICIENCY
THROUGH DATABASE DESIGN
Problem Definition
The employees at ERICA have begun to take the
roles of consultants to outside customers. A company
or individual who has a specific need for the system
requires particular demands of ERICA, Inc. These
employees then analyze the request and custom fit the
system to the needs of the customer.
The original design of the ERICA system was found
to be inefficient. The software employed a file system
in which all recorded data was saved in an assortment
of file extensions. Each time the developers needed to
access the information, they had to sort through a large
number of file types. Additionally, the system required
an intense level of code for each new type of data
retrieval. Whenever a customer submitted a request
concerning a specific type of data analysis, the
employees at ERICA had to painstakingly hard code the
analysis options into the code of the ERICA software.
This process proved to be long and tedious.
One responsibility of the ERICA, Inc. Capstone
Project Team was to improve the ease of development
of an ERICA software application, called GazeTracker.
This program has proven to be the most development
intensive application for the ERICA programmers.
GazeTracker is responsible for two functionalities using
the ERICA system. First, the “Slide Show” function
observes a user’s gaze response to a slide show of still
images. It allows for the complete recording and
playback of where the user is looking as different
images are displayed on the computer screen. Figure 6
includes a picture of a print advertisement after it has
been viewed in a “Slide Show” analysis. A line shows
where the viewer was looking on the advertisement
over time. Second, the “Application Analysis”
functionality allows for the recording of gaze direction
as other Windows applications are used. On playback,
GazeTracker can display each application as it is
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Figure 6 “Slide Show” analysis example
In order to improve the ease of development of
GazeTracker, we designed and developed what is called
an object-relational database system. This system
provides an object-oriented programming environment
to save and retrieve data to and from a relational
database. That is, it allows the ERICA programmers to
send an object (i.e. application data member) to be
saved without having to worry about how or where it is
saved. The object-relational system breaks this object
down into smaller data types that can be saved in a
relational database.
The database behind the GazeTracker application is
divided into two database files representing the two
functionalities of GazeTracker and a section of code
used to access them. For the purposes of this
document, the Slide Show database, BmpBase, deals
with the storing and organization of data relating to the
Slide Show functionality of GazeTracker. Similarly,
the application database, AppBase, deals with data
relating to the application functionality in GazeTracker.
The combination of BmpBase, AppBase, and the code
used to access them is appropriately called GazeBase.
In order to complete this project, we utilized the
help of Jason Rudolph, a Computer Science major
working with ERICA, Inc. He assisted us by writing
the code in Visual C++.
Database Design and Development
The database design and development consisted of
four major stages. In the first phase, specification and
requirements, we identified what data were to be stored
in the database. In the second stage, system
architecture, we organized the major components of an
1999 Systems Engineering Capstone Conference • University of Virginia
object-relational database system. In the third stage, we
created the database on paper with a series of design
documents. Finally, we implemented the design of the
database in Microsoft Access 97. The following
sections will explain these stages in more detail
Specifications and Requirements.
This stage of the design allowed us to create a list
of variables that were to be stored in the database and to
understand the relationships between the variables.
This work consisted of learning how GazeTracker
works, learning the code behind the application, and
interviewing the ERICA personnel about their
expectations of the database system.
Systems Architecture
The database architecture defines the main
components of the database system and how they
interact. The object-relational database system is
composed of three components -- the relational
database, its encapsulating code, and the GazeTracker
application.
The reason for using this type of database system is
as follows. A relational database alone cannot store
user-defined types of variables such as those used in
GazeTracker. Each instance of a class must be broken
down into their member variables to be saved. Then,
when the information is retrieved from the database, the
variables must be recreated using the stored member
variables.
Figure 7 provides a pictorial representation of the
Database system. As can be seen in the figure, there
are no direct links between GazeTracker and the
databases. The application contains instances of classes
that contain member variables that must be saved and
retrieved from a database. The encapsulating code
(named Communication Component in Figure 7)
performs these communications between the
application and database.
System Design
We created four design documents that would help
us develop the database system. The first document,
the Class Object Model, gave a pictorial representation
of the data in GazeTracker. It included the variables
organized by class and the relationships between the
classes. The Relational Layout showed the
relationships between these data variables, as they were
to be included in the database. The third document, the
Data Dictionary, provided definitions for each table and
field in the database. Finally, the Data Characteristics
Document indicated whether or not each field was a
required field, gave field lengths for text data types, and
identified all primary and foreign keys.
System Development
After the first iteration of the database design,
using Microsoft Access 97 and the original versions of
the Relational Layout, Data Dictionary and Data
Characteristics Document, we developed the first
version of BmpBase. With each design iteration
thereafter, we reflected any necessary changes in the
Access database. After a number of iterations, we
completely implemented the current version of
BmpBase. While we implemented the database, Jason
Rudolph wrote the encapsulating code to support the
communication between it and GazeTracker.
We are currently developing AppBase and its
associated encapsulating code to support the data
saving and retrieval for the Application Analysis
functionalities of GazeTracker.
Results and Conclusions
Prior to this project, ERICA Incorporated
employed a complicated and inefficient file saving
system using numerous file extensions to save different
types of data. The system was complicated because it
caused the software developers to keep track of a large
number of file types at one time. It was inefficient
because it required a large amount of code for the
saving and retrieval of data in each new version of the
software. This system prohibited the ERICA
programmers to maintain development efficiency.
In an effort to begin solving this problem, we have
produced the backbone of the data storing in the
GazeTracker application. The object-relational
architecture allows for easy data storing and retrieval.
The project has also produced a significant amount of
documentation that will allow future database
developers at ERICA, Inc. to easily maintain and revise
the system.
Figure 7 Object-Relational DB System Architecture.
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Erica, Inc. Capstone 1998-1999
CAPSTONE PROJECT CONCLUSION
BIOGRAPHIES
During the 1998-1999 school year, the Capstone
Team members, Dayna Balcome, Peter Bokach, and
Justin Petersen, worked to expand the ERICA system’s
potential to compete in various markets. We succeeded
in introducing ERICA into the field of deception
detection. In particular, the Department of Technical
Services at the Central Intelligence Agency and the
U.S. Department of Defense are interested in our work.
We also demonstrated the system’s capability to
analyze print advertisements and improved the
marketability of the system in the advertising industry.
The Gaze-base project improved the flexibility of
ERICA, Inc.’s software so that ERICA, Inc. employees
can respond to new customer requirements more
efficiently.
Dayna Balcome is a fourth-year Systems Engineering
major from Charleston, SC. Her principal contribution
to the project was in the area of developing the ERICA
system as a deception detection device. Ms. Balcome
has accepted a position with Ernst & Young LLP in
New York, NY, and will begin training in the
Management Consulting division in August.
REFERENCES
Bradley, M.T., V.V. MacLaren, and M.E. Black. 1996.
“The Control Question Test in Polygraphic
Examinations with Actual Controls for Truth.”
Perceptual and Motor Skills, 83: 755-762.
DeMars, Geneva. 1997. “Lie Detection: An ERICA
Application.” Undergraduate Thesis. University of
Virginia.
DeSanti M., and J. Gomsi. 1994. "A comparison of
object and relational database technologies." Object
Magazine 3.5 (January): 51, 56-57.
Ford, Charles V. 1996. Lies! Lies!! Lies!!! The
Psychology of Deceit. American Psychiatric Press, Inc.,
Washington DC.
Kahneman, D. and Beatty, J. 1966. Pupil Diameter and
Load on Memory. Science, 154: 1583-1585.
Lubow, R.E. and Fein, Ofer. 1996. “Pupillary Size in
Response to a Visual Guilty Knowledge Test: New
Technique for the Detection of Deception.” Journal of
Experimental Psychology: Applied. 2, No.2: 164-177.
Rational Rose. “Integrating Object and Relational
Technologies.” Rational Rose Web Site. 1998.
<http://www.rational.com/support/techpapers/object_re
lational.html#What_Creates_Need_ORI>
Roper Starch Worldwide Inc. “Starch Readership
Report.” Glamour. September 1998.
“A User’s Guide to ERICA.” 1999. Property of ERICA
Inc.
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Peter Bokach is a fourth-year Systems Engineering
major from Columbus, Ohio. His principal contribution
to the project was determining the effectiveness of
ERICA as an advertising tool and comparing his test
results with an industry standard, Roper Starch Reports.
Mr. Bokach has accepted a position with the
Management Consulting division of
PricewaterhouseCoopers in Los Angeles, CA. He will
begin working in September of 1999.
Justin M. Petersen is a fourth-year Systems
Engineering major from Chapel Hill, NC, concentrating
in Computer Information Systems. His principal
contribution to the ERICA Capstone Project was in the
area of database system design and implementation.
Mr. Petersen has accepted a position with the Atlanta,
GA office of Ernst & Young Consulting LLP and will
begin working in September of 1999.