EFFECT OF FONT AND BACKGROUND COLOR COMBINATION ON THE
RECOGNITION EFFICIENCY FOR LCD DISPLAYS
A Thesis Presented
By
Zeliang Cheng
to
The Department of Mechanical and Industrial Engineering
in partial fulfillment of the requirements
for the degree of
Master of Science
in the field of
Industrial Engineering
Northeastern University
Boston, Massachusetts
May 2015
ii
Table of contents:
1. Introductions ............................................................................................................ 1
1.1 General backgrounds ........................................................................................ 1
1.2 The influence of colors on the RE .................................................................... 3
1.3 Literature reviews ............................................................................................. 4
1.4 Study goals ........................................................................................................ 7
2. Methods ..................................................................................................................... 8
2.1 The Primary Color Difference .......................................................................... 8
2.2 Study hypotheses .............................................................................................. 9
2.3 Measurement of the RE .................................................................................... 9
3. Data Collection and Experimental Design ........................................................... 11
3.1 Participant ....................................................................................................... 11
3.2 Testing Program .............................................................................................. 11
3.3 Testing Procedures .......................................................................................... 13
3.4 Experiment setup and protocols ...................................................................... 16
3.5 Experimental design........................................................................................ 17
4. Data Analysis .......................................................................................................... 20
4.1 Main factors .................................................................................................... 20
4.2 Interaction between factors ............................................................................. 21
4.3 Response Surface Methodology ..................................................................... 23
5. Conclusions and Discussions ................................................................................. 24
References: .................................................................................................................. 28
iii
Acknowledgements:
Without the generous advices and helps offered by my academic advisor
Professor Yingzi Lin, this graduate thesis could not be completed so smoothly. I
hereby express the deepest appreciation for Professor Lin’s time and efforts. I also
want to extend my appreciation to my classmate Vahab Vahdatzad, who contributed
his time and wisdom to the experiment design and the data analysis parts of this thesis
and whose name will be appeared as the second author of this thesis if this thesis be
published on other journals.
iv
Abstract:
User interface (UI) is an important media that connects us with other physical
systems. With the popularization of computers, the Liquid Crystal Displays (LCDs) as
a main type of information output terminal for computers have become one of the
main types of UI. With a general goal of improving the recognition efficiency (RE) of
LCDs and reducing human observers’ information reading errors, this paper studies
the effect of font/background colors on the RE of texts being presented on LCD.
In this study, a new concept of the Primary Color Difference (PCD) was put
forward to define the font/background color difference, which enables us to study the
relationship between the RE and the font/background colors in a more efficient way.
Results shown that the PCD has a significant effect on the RE of text being presented
on the LCDs. Besides, certain PCD values give the texts a better RE than other PCD
values do. Furthermore, font/background color combinations that share similar PCD
values may also share certain color properties which result in the similar RE for the
text being presented on the LCDs.
Key words: User Interface, Recognition Efficiency, Font/Background Colors,
Primary Color Difference.
1
1. Introductions
1.1 General backgrounds
User interface (UI) is an important media that connects human with other
physical systems [Zhang et al., 2007]. Nowadays, with a trend that computers have
being widely used as a control and monitoring terminal in many industries, human-
computer interface (HCI) has become a main type of UI. Typical HCIs include Liquid
Crystal Displays (LCDs), Light Emitting Diode (LED) displays, indicator lights and
even speakers, among which electronic displays such as the LCDs are the most
common ones [Humar et al., 2008]. As an example, in the current healthcare institutes
the media for information flow is now transitioning from paper-based to computer-
based [Beuscart-Zéphir et al., 2010], which results in a growing number of
information being presented on LCDs. In this case, a proper design of the way
information being displayed on LCDs could decrease the human errors of information
reading and hence prevent fatal errors [Lawler et al., 2011]. With a general goal of
improving the human-computer interaction efficiency, this paper aims to improve the
visual performance of LCDs, one of the most common HCIs, by studying the way
texts being presented on them.
One important indication of visual performance of certain text presented on LCDs
is its Recognition Efficiency (RE) [Dyson, 2004]. The RE is further related to the
text’s readability or legibility. Readability is the reader’s ability to recognize the form
2
of a word or a group of words for contextual purposes [ANSI/HFS 100-1988], while
the legibility is the reader’s ability to recognize the form of single word without
contextual purposes. In a common sense, the more legible words or texts are being
presented, the higher their RE is. In this paper however, we examine only on the non-
meaningful and randomly composed three-letter word stimuli in order to eliminate the
learning effect. A higher RE means the word stimulus is more legible. More detail
about the stimuli studied in this paper will be explained later.
The texts’ RE has been confirmed to be influenced by factors such as their
luminance, font size, font style and text layout by previous researches. For example,
luminance was found to have major effects on the RE [Bruce and Foster, 1982; Small,
1982; Legge and Rubin, 1986]. Researchers such as Lin (2005) not only found that
the combination of luminance and contrast ratio had a significant effect on RE, but
also pointed out the two factors might have some correlations that merit further study.
The font type is another factor that may influence the RE, and for different types of
languages the influences may be different. Researchers studied on English characters
[Ling et al., 2006; Huang et al., 2009; Shen et al., 2009] found that there was no
significant effect of commonly used font types on RE while researches on Chinese
characters [Cai et al., 2001; Lai et al., 2010; Yau et al., 2009] indicated that significant
effect was found between these two factors. Furthermore, the combination of font size
and font type may also influence the RE [Ramadan, 2011].
3
Besides of these factors, color is another important factor affecting the RE.
Yussof et al. (2012) drew a conclusion that the background color gives affection to RE
of texts on e-books. This conclusion was also supported by Bonnardel et al. (2011)’s
research on the website design. Some researchers also pointed out that inappropriate
use of color could result in poor performance and a higher incidence of visual
discomfort [Murch, 1985; Matthews and Mertings, 1987].
1.2 The influence of colors on the RE
Before looking further into the effect of colors on the RE, we should define colors
first. A useful way to define colors is using the widely accepted concept of “primary
color”, which define any observable color by the combination of the three primary
colors. Considering the difference between the physical properties for reflector
materials (such as papers) and luminophor materials (such as LCDs we examined in
this paper), the primary color’s definition is different on this two materials. For colors
on reflectors, the three main colors are “Red, Yellow and Green” while for colors on
luminophor the three main colors are “Red, Green and Blue”, or RGB. Because our
target is LCD, a typical kind of luminophor, we use RGB as our color definition scale
plate.
For any color shown on LCDs, its RGB has 256 different values (indicated by
consecutive integers from 0 to 255). So colors on LCDs can be defined by its three
4
RGB values, respectively. For instance, the pure black can be defined as Red=0,
Green=0, Blue=0 or (RGB=0, 0, 0); the pure white can be defined as Red=255,
Green=255, Blue=255 or (RGB=255, 255, 255). The black and the white are two
extreme cases of colors. Other colors’ RGB values fall between 0 and 255. For
example, the sky blue can be defined as (RGB=0, 155, 255).
If we could make a general rank about the virtual performance of
font/background color combinations in terms of their RE, our goal to compare the RE
under different font/color combinations would be realized. In other word, we should
get a complete ranking of the RE for all the possible color combinations. However, as
we could imagine, the number of colors defined by RGB could be more than 16
million (256×256×256), let along the combination between any two of them (more
than 200 trillion combinations). Thus we must find a more efficient way to realize the
goal rather than enumerate all the possibilities.
Previous studies on the effect of font/background color combinations on the RE
simply hand pick several typical color combinations to determine which combination
is better than the others, which lacks generality.
1.3 Literature reviews
The first known research focused on the reflector media to rank texts’ RE
5
according to their font/background color combinations was conducted in 1912 by Le
Courier [Le Courrier du Livre, 1912]. He performed an experiment in which letters
were printed on paper posters under different font/background color combinations.
Each poster contained two rows of letters. All posters were exposed to sunlight and
participants were asked to rank the legibility of the letters. The ranking of 13
font/background color combinations was then listed in a table, which was known as
Le Courier legibility table as shown in table 1.1. The higher the legibility was ranked,
the better texts’ RE would be. Later on between 1928 and 1963, Tinker and Paterson
carried out a similar research of legibility and readability on reflector [Tinker and
Paterson, 1929]. They studied the legibility of text under 10 font/background color
combinations and gave another ranking. As we can see, their ranking is differed from
the ranking in the Le Courier table. But this discrepancy is not typical. Even today,
researchers can hardly come to an agreement about which color combination is
statistically better than others under different experimental conditions.
Table 1.1: Le Courier’s legibility table
Ranking 1 2 3 4 5 6 7 8 9 10 11 12 13
Font Color BK G R B W BK Y W W W R G R
Background Color Y W W W B W BK R G BK Y R G
Note: “R” is Red, “G” is Green, “B” is Blue, “BK” is Black, “W” is White and “Y” is
Yellow.
6
Another limitation of previous studies is that because luminophor’s physical
properties are different from those for reflectors, conclusions from previous studies
can hardly be directly extended into the context of luminophor such as LCDs. For
instance, some researchers reported that the visual performance was slower with
electronic display than with paper material [Gould et al., 1987; Dillon, 1992]. Also,
they pointed out that electronic displays might lead to visual fatigue and strain more
quickly and frequently. In 2008, Humar et al. performed an experiment similar to Le
Courier’s but on electronic screens rather than on paper posters. The purpose of their
study was to examine the visual performance of webpages [Humar et al., 2008]. They
examined a number of font/background color combinations, and changes in the
ranking of the same color combination can be observed accordingly, as listed in table
1.2. Although earlier researchers failed to identify specific color combination that
performs a better readability on electronic displays, they revealed that the
font/background color combination is an important factor that affects RE [Radl, 1980;
Pace, 1984].
Table 1.2: Comparison of Humar’s and Le Courier’s legibility table
Font Color BK G R B W BK Y W W W R G R
Background Color Y W W W B W BK R G BK Y R G
New Ranking 7 5 1 10 6 4 11 8 3 13 2 9 12
Old Ranking 1 2 3 4 5 6 7 8 9 10 11 12 13
Note: “New Ranking” is Humar’s ranking; “Old Ranking” is Le Courier’s ranking.
7
Finally, the methods used by previous researchers were also different from one
another. We can classify these methods into three main categories with regard to how
to measure the participant’s visual performance [Humar et al., 2008]. The first method
is called visual search tasks [Small, 1982; Pace, 1984; Pearson and Schaik, 2003].
One typical application of this method is that participants had to either identify
misspelled words or find special words from non-meaningful paragraphs. The second
method is to measure the recognition time for certain stimuli (characters, words or
sentences) [Bruce and Foster, 1982; Pastoor, 1990; Wu and Yuan, 2003; Ramadan,
2011]. The third method is to measure participant’s accuracy in words recognition
tasks. The stimuli were shown to the participants either for a very short time or in a
relatively small font size and the participants were either asked to identify them or
write them down as many as possible during the task [Travis et al., 1990; Schieh et
al., 1997; Schieh and Lin, 2000; Wang and Chen, 2003]. In this study however, we
adopt the second method in measuring the recognition time for each stimuli, which
will be elaborate in the following parts.
1.4 Study goals
Our general goal is to find a more efficient way to examine the effect of the
font/background color combinations on the RE and to tell from statistical perspective
which color combination is better than the other.
8
2. Methods
2.1 The Primary Color Difference
As we mentioned above, colors on luminophor materials can be defined by the
three primary colors of RGB with each ranging from integer value 0 to 255. Given
any two colors, we could further define their Primary Color Difference (PCD) as the
absolute difference between each of their three primary colors respectively: namely
the ΔRed (ΔR), the ΔGreen (ΔG) and the ΔBlue (ΔB) (Δ represent absolute
difference). The value of the ΔR, the ΔG or the ΔB for any two given colors will fall
between 0 and 255.
As a result, we could adopt the concept of the PCD to define the font/background
color difference. For example, the orange color font (RGB=255, 100, 0) on pure white
background (RGB=255, 255, 255) can be defined as ΔR=0 (255–255), ΔG=155 (|100–
255|) and ΔB=255 (|0–255|), or (ΔRGB=0, 155, 255). But the reverse is not
necessarily true. A PCD of (ΔRGB=0, 155, 255) could represent the color
combination of any pair of colors with the same primary color difference. For
instance, the PCD of the sky blue font (RGB=0, 155, 255) on pure black background
(RGB=0, 0, 0) is also (ΔRGB=0, 155, 255).
9
2.2 Study hypotheses
In this paper, we study the font/background color by focusing only on their PCD
values regardless of what color combination it may represent for. One of our
hypotheses is that although certain PCD value can stand for many font/background
color combinations, these combinations that share a same PCD value may have
certain color properties in common and results in the similar RE. If this hypothesis is
proved true, we could realize one of our goals to find a more efficient method to study
colors rather than enumeration.
As we could tell from previous studies, text’s visual performance, or the RE,
under different font/background color combinations is different from one to another.
For instance, pure black text on pure white background is easier to read for majority
of people than yellow text on white background [Xue-min Zhang et al., 2007]. Thus,
another hypothesis is that there must be some relationship between the PCD value and
the RE.
2.3 Measurement of the RE
We designed an experiment to measure participants’ RE to certain text under
different font/background color combinations (see experiment design part for more
details). Participants were requested to respond to the font stimuli shown on the
screen as fast as they can. Similar to previous studies, we define the RE by
10
participants’ average Response Time (RT) to the target stimuli.
There are some discussions about how to interpret RE using RT. For example,
because RE itself is a “subjective” concept, it is hardly to establish one-to-one
correspondence between each REs and their RTs. Different people have different
observations. One possible reason for this diversity is that our ability to mentally
processing the environmental stimulus is different from one to another. Furthermore,
the attitude of different individuals towards the outside of the world is also different
and no evidence show that such a bias has not effect on their RE. For example,
individuals who show a greater preference to green color may have a quicker response
to greens. To better address this problem, we use random sampling in selecting the
participants in terms of their gender, ethnics and backgrounds.
Moreover, people’s RE for certain stimulus may change with time and their living
environment. One possible explanation is that people’s mental and physical status will
change with time. Besides of these subjective factors, environmental factors such as
light, temperature and noise may also have significant effect on people’s RE to certain
stimuli. So we fixed all environmental factors for all participants to achieve results
focused on font/background color effects on RE and RT.
Another major problems we faced was how to measuring RT accurately. The
average RT in our experiment was <1s for the given stimuli. Besides, it was hard to
11
tell exactly when or whether a participant recognizes the stimulus. Recognition as a
mental process is hard to be observed with a “threshold” of appear and gone. Even the
participant his/herself cannot tell exactly when he/she distinguish or recognize the
stimulus.
In order to solve these problems, we wrote a computer program to assist the
experiment in order to record RT with high accuracy and stability. Another reason to
use computer program was that it enables us to control variables easily. More details
about the program will be explained in part 3.2.
3. Data Collection and Experimental Design
3.1 Participant
We sent invitations randomly to Northeastern University students and we
randomly selected 11 qualified invitees to participate in the experiment. All
participants are current student (Average age=25, 6 Male, 5 Female). All participants
are with normal vision or corrected to normal vision.
3.2 Testing Program
In order to record subjects’ RT to certain stimuli accurately, a computer program
12
is written in Microsoft Visual Studio C# platform. A structural procedure to measure
RT is introduced. Factorial design experiment platform is used to perform
experiments using proposed testing application.
The program enable us to control variable by changing the text's font/background
color regarding to its RGB values. A graphical user interface is designed to help
participants understand how experiments work. Figure 3.1 shows the interface of the
program.
Nine (9) stimuli each consist of three randomly selected letters are mapped to a
numeric keypad as the target text. ATS is mapped to 1, OTR to 2, OPS to 3 and so
forth, as shown in Figure 3.1. During the experiment, these stimuli will randomly and
individually being presented to participants in certain font/background colors. To
minimize the learning effect of participants predict the stimulus without read it
completely, these nine stimuli share some similar characters such as same starting or
ending character.
The testing program was also tested to verify its user friendliness. A systematic
approach to collect the data (in terms of RT) was approved, which will be elaborated
in the following section.
13
Figure 3.1
3.3 Testing Procedures
We assigned one font/background color combination for each run of the test. The
program will run to show participants the nine stimuli under the assigned
font/background color combination and collect the participants RT to these stimuli.
We replicated the testing procedure several times to achieve more accurate data.
The testing process is shown in Figure 3.2 below as an example of
font/background color combination as green/black. First, a crosshair cue appears in
the center of the screen for 200 milliseconds to indicate the position that stimulus will
appear. Then, a randomly selected stimulus of green appears at the same position 200
milliseconds after the crosshair hide. In each experiment, a participant will be asked
14
to press the space button on the keyboard as soon as recognizing the stimuli. Once the
participant pressed the space button, the stimulus text disappears, living only the
background. The time interval from the stimulus appear on display to the participant
press space button, is recorded as the response time (RT). After the participant pressed
the space button, the program waits until participant enter the stimulus mapped
number to check recognition correctness. For example, if the participant recognized
the “RAP” stimulus, he/she should press the mapped number button “1” in the
keyboard; if the participant recognized the “ROT”, he/she should press the number
button “2”, and so forth. The testing procedure will be repeated until enough data
being collected regardless the confirmation typing is correct or not. However, if the
participants' incorrectness rate exceed 5%, the data collected in this run will be
discarded. Stimulus words combinations were selected in a way that participants could
not easily guess the stimulus word unless he or she recognizes all its three letters. For
instance, two of the nine stimuli start with letter “R”, two of them start with letter “S”;
two of the nine stimuli end with letter “P”, two of them end with letter “T”, and so
forth.
15
Figure 3.2
Initialize
Show only the
background for 1000ms
Show crosshair on the
background for 200ms
Show only background
again for 200ms
Show the font stimulus
and begin timing
Stimulus disappear when
the subject pressed the
Space buttom. Stop timing
Test finished?
Test finished
YES
NOAPR
16
3.4 Experiment setup and protocols
Prior to performing any experimental design, careful considerations of factors
that might affect test results are inevitable [Ader, Mellenberg & Hand, 2008].
Environmental conditions, experiment conditions and instruments are examples of
conditions and factors that should be controlled. Some of these factors might as well
have impacts on the test’s legibility, they are not fitted into our research scope and can
be considered in further researches.
Environmental factors such as light intensity and audible noise were fixed during
the experiments by controlling the laboratory conditions. The experiment instruments,
screen size, screen type, screen resolution, computer hardware specifications,
keyboard type, chair type and height, and table height were same for all participants.
In terms of selecting LCD displays’ type and specifications, most prevalent
displays’ settings were considered. LCD displays with 1920×1080 resolution and
screen refresh rate of 60Hz, diagonal viewing Size of 24 inches with preset display
area (H×V): 20.9×11.7 square inches were used during the experiments.
We controlled laboratory conditions such as distance of participants to the
display, display’s angle, position of display and keyboard. In order to control variables
of the relative position between subjects and the display, all participants were required
to sit straight comfortably on an adjustable rotary chair in front of the display. The
17
subjects were also required to adjust the display and the chair properly that make sure
their sight lines are as perpendicular to the center of the display plane as possible. The
ergonomic designs suggest 50 to 70 centimeters distance between eyes and screen and
10 to 20 centimeters lower than user eyesight. Participants were asked to familiar with
the testing software and the testing procedure before the test.
3.5 Experimental design
The study was designed as full factorial design. As shown in figure 3.3. The first
factor was “background color” with 2 values that were white (RGB=255, 255, 255)
and black (RGB=0, 0, 0). The second factor was “font color” with 9 values defined by
the primary color difference (PCD) that are: three ∆Rs (∆R=250, ∆R=180, ∆R=110);
three ∆Gs (∆G=250, ∆G=180, ∆G=110); and three ∆Bs (∆B=250, ∆B=180, ∆B=110).
For each combination of background and font color, the test was repeated 10 times.
That means each participants were asked to perform all 18 experiments (2×9) with 10
replications each (18×10=180 experiments in total), which last about 35 minutes.
Number of participant that completed the tests were 11 and the overall number of tests
was 1980 (2×9×10×11).
18
Figure 3.3
∆B=180
∆B=250
∆G=110
∆G=180
∆G=250∆R=110
∆R=180
∆R=250
∆B=110
White
Black
2
1
Font color
9
8
7 6
5
4
3
2
1
Font color
Full factorial
experimental design
According to Fisher et al., any experiments conducted under statistical Design of
Experiments (DoE) framework have to follow three principals (i.e. randomization,
replication, blocking) in order to achieve a reasonable and valid finding [R. A. Fisher,
1935; D. C. Montgomery, 2008]. We also applied these techniques in our design.
Randomization is the process of assigning individuals randomly to one or group
19
of experiments. In our experiments the order of allocating font/background color was
randomized. A proper randomization approach helps “averaging-out” impacts of
extraneous factors that might present. Replication decrease impacts of variability by
repeating each factor combination independently. It also helps obtaining an
approximation of experimental error. For this reason, for the same font/background
color the test was repeated 10 times although the actual text stimuli were different
during the replication process. Finally, blocking is a design technique that arranges
experimental units into a group to improve comparison precision and eliminate known
variation. We used blocking to group results by participants. Some participants had
faster reactions than others. By grouping each participant, a set of relatively
homogenous experimental conditions were compared.
20
4. Data Analysis
For analyzing the data, Factorial Analyses of Variances (ANOVA) were
conducted to compare mean response time to each combination of font/background
color. All analysis was performed with Minitab 15. ANOVA results revealed that not
only the main factors (i.e. font and background color) have significant effects on
text’s legibility, interactions between factors do too.
4.1 Main factors
Font/background colors as main factors were compared by ANOVA. There was a
significant difference between two background colors (ANOVA, F= 911.55, p = 0.00)
as well as between different font colors (ANOVA, F= 3385.06, p = 0.00).
In addition to these factors, ∆ values were compared. Through grouping font
colors by their ∆ values (i.e. ∆=250, ∆=180, ∆=110) rather than ∆Rs, ∆Gs, and ∆Bs
we were able to further express impacts of ∆ values on the legibility. There was a
significant difference between ∆ values (ANOVA, F= 263.50, p = 0.00), though F-
value was comparatively lower. In this case, the R-Sq value was high enough (R-Sq =
83.28%) to assure the data was fitted well to ANOVA statistical model.
Figure 4.1 depicts mean response time for each factor. Green had least response
time followed by red and blue. Also response time to black background color was less
than white.
21
Figure 4.1
4.2 Interaction between factors
If interactions between factors exist, differences in response value (i.e. RT)
among levels of different factor might not be the same [D. C. Montgomery, 1935; R.
A. Fisher, 2008]. In other words, interaction is the failure of one factor to generate
same impacts on the response value at different levels of another factor.
22
Hence, we analyzed all possible interactions of factors to interpret significant
changes in response time. Figure 4.2 illustrates all possible interactions. The
interaction plot between background and font color is shown in the top right. As we
can see, there was a significant interaction effect between the two main factors. For
other colors (i.e. red and blue) interaction between factors led to different response
time when levels of one factor changed. The interactions between ∆ values and font
colors are shown in middle right. In all delta values, green font color had least
response time following by red and blue respectively. ∆ values and font colors had not
significant interaction and the previous trend were remained unchanged. Also, there
was not a significant interaction between background color and ∆ values on response
time as shown in middle top of the figure.
Figure 4.2
900
600
300
RGB
250180110
900
600
300
WB
900
600
300
BG color
Delta
Font color
B
W
BG color
110
180
250
Delta
B
G
R
color
Font
Interaction Plot for Response timeData Means
23
4.3 Response Surface Methodology
Response Surface Methodology (RSM) provides deeper explorations among
associated control variables to one or more response of interest. In general, RSM
includes of group of statistical and mathematical techniques used in building an
unknown functional relationship between response variables and a number of input
variables. Fitting response curve to the levels of a quantitative factor can be beneficial
to interpret that relationship, predict response value with different combinations of
design factors, and determine the optimum setting of variables to minimize (or
maximize) response value.
Figure 4.3 shows how changing font colors and ∆ values concurrently, might
affect response time. The best response time area achieved by ∆ values within 220 to
250 intervals. This is a good expansion of our results since ∆ values as well as font
colors can be inferred as continues variables. Note that minimum response time
achieved when font color is between green and red.
24
Figure 4.3
5. Conclusions and Discussions
In this study, we examined the effect of font/background color combination on
the Recognition Efficiency (RE) via a new concept of Primary Color Difference
(PCD) between the two colors. We conducted the experiment with a Design of
Experiment of 2×9 levels: 2 background colors (White and Black), and 9 PCD values
between a given background color and the font colors (ΔR=250/180/110,
ΔG=250/180/110, ΔB=250/180/110).
Results reveal that the background/font color combination has a significant effect
on the RE. Generally speaking, black background performs better than the white
Font Colors
De
lta
3.02.52.01.51.0
240
220
200
180
160
140
120
BG colors 1
Hold Values
>
–
–
–
–
–
< 200
200 300
300 400
400 500
500 600
600 700
700
time
Response
Contour Plot of Response time vs Delta, Font Colors
25
background in terms of the RE. Besides, when maximizing the ΔG value of the
font/background combination the RE could be greatly increased, maximizing the
same amount of ΔR shows relatively less of such an increase, while maximizing the
same amount of ΔB shows even some decrease in the RE. Thus the effect of PCD on
the RE can be shown as “ΔG>ΔR>ΔB”. Moreover, font/background color
combinations with a higher PCD value of either ΔG, ΔR or ΔB shows a better
performance of the RE compared with those color combinations with a less PCD
value. According to the ANOVA analysis, not only the font color, background color
and PCD values, but also the interactions between these factors have significant effect
on the Recognition Time (RT).
Some of these conclusions can be intuitively shown in figure 5.1 (in example text
“Hello”). In this example graph, each row indicates a certain PCD value (ΔR, ΔG and
ΔB of either Δ=250, 180 or 110). The left half of the graph is on black background
and the right half is on white background. According to our conclusions, the text
“Hello” on black background is generally more distinguishable than that on white.
Besides, in general, texts with its ΔG maximized are more distinguishable than those
with ΔR maximized or those with ΔB maximized. For each of the three PCD groups
in the graph, the texts on the first row are more distinguishable than the texts on the
following rows while the upper group (ΔG) shows a more clear of such trend
(ΔG>ΔR>ΔB). Font with the ΔG maximized with Δ=250 is most distinguishable
while font with the ΔB maximized with Δ=110 is least distinguishable. These
26
subjective feelings accord with our statistical results.
One of the most important applications of our conclusions is that we could
maximizing certain text’s RE by adjusting only the text’s font color so that the ΔG or
ΔR values between the text’s font/background colors be maximized.
Figure 5.1
One inference from the conclusion is that the RE of the text on certain
background color can be maximized when increasing both the ΔG and the ΔR at the
same time. Further research should be done on this inferences though.
27
One innovation of this study is that we put forward a new concept of PCD in
studying the virtual performance of the user interface. By considering the PCD
instead of the specific font/background colors, we greatly reduce the sample space of
all the possibilities to be examined. Most importantly, this study offers a realizable
way to maximizing the RE for any given color.
This study also has several limitations. First, all of the participants are students
between age of 19-28 and we only have 11 participants. We could involve more
participants with a broader range of ages in the further study. Besides, we examine the
value of ΔR, ΔG, and ΔB separately without considering the consequences that two or
three of the PCD values change together. Furthermore, due to the limited time, the
testing program may have some undiscovered flaws that could affect the accuracy of
the data collected. Finally, we only considered one of the color’s property (primary
colors) rather than all other physical properties such as the saturability.
28
References:
Ader, Mellenberg & Hand (2008) “Advising on Research Methods: A consultant's
companion”.
An-Hsiang Wang, Chih-Chen Tseng, Shie-Chang Jeng, Effects of bending curvature
and text/background color combinations of e-paper on subjects’ visual performance and
subjective preferences under various ambient illuminance conditions, 2007
ScienceDirect, 161-166.
ANSI/HFS 100-1988, (1988) American National Standard for Human Factors
Engineering of Visual Display Terminal Workstations. The Human Factors Society, Inc.
Bruce, M., Foster, J.J., 1982. The visibility of colored characters on colored
backgrounds in view data displays. Visible Language 16 (4), 382–390.
Chien-Jung Lai, Effects of color scheme and message lines of variable message signs
on driver performance, Accident Analysis and Prevention 42 (2010) 1003-1008.
Chien-Jung Lai, Kuo-Duan Yen, Duan-Bing Wang, Effects of Chinese font style and
color on variable message sings, Proceeding of the Fourth International Driving
Symopsium on Human Factors in Drive Assessment, Training and Vehicle Design.
Chin-Chiuan Lin, Effects of screen luminance combination and text color on visual
performance with TFL-LCD, International Journal of Industrial Ergonomics 35 (2005)
229-235.
29
Creswell, J.W. (2008). Educational research: Planning, conducting, and evaluating
quantitative and qualitative research (3rd). Upper Saddle River, NJ: Prentice Hall. 2008,
p. 300. ISBN 0-13-613550-1.
D.A.Norman, The Design of Everyday Things, Basic Books, New York, 2002.
D.C. Montgomery, Design and analysis of experiments: John Wiley & Sons, 2008.
Dengchuan Cai, Chia-Fen Chi, Manlai You, The legibility of Chinese characters in
three-type styles, International Journal of Industrial Ergonomics 27 (2001) 9-17.
Dillon, A., 1992. Reading from paper versus screen: a critical review of empirical
literature. Ergonomics 32, 1297–1326.
Ding-Long Huang et al., Effect of font size, display solution and task type on reading
Chinese fonts from mobile devices, International Journal of Industrial Ergonomics 39
(2009) 81-89.
Erin K. Lawler et al., Cognitive ergonomics, socio-technical systems, and the impact
of healthcare information technologies, International Journal of Industrial Ergonomics
41 (2011) 336-344.
Fisher, Ronald Aylmer, 1935,“The design of experiments.” The design of experiments.
30
Gould, A.J.D., Alfaro, L., Barnes, V., Finn, R., Grischkowsky, N., Minuto, A., 1987.
Reading is slower from CRT displays than from paper: attempts to isolate a single-
variable explanation. Human Factors 29 (3), 269–299.
Graham B.Erickson et al., Readability of a computer-based system for measuring visual
performance skills, 2011 Optometry 82, 528-542.
Hani (2009). “Replication study”, retrieved 27 October 2011.
Humar et al., 2008. The impact of color combinations on the legibility of a Webpage
text presented on CRT displays. International Journal of Industrial Ergonomics, 38
(2008) 885–899.
I-Hsuan Shen et al., Lighting, font style, and polarity on visual performance and visual
fatigue with electronic paper displays, Displays 30 (2009) 53-58.
Jonathan Ling, Paul and Schaik, The influence of font type and line length on visual
search and information retrieval in web pages, Int. J. Human-Computer Studies 64
(2006) 395-404.
Le Courrier du Livre, 1912. Lisibilite des Affiches en Couleurs. Sheldons Limited
House, Cosmos, p.255.
Legge, G.E., Rubin, G.S., 1986. Psychophysics of reading: IV. Wavelength effects in
normal and low vision. Journal of the Optics Society of America, A (3), 40–51.
31
Legge, G.E., Rubin, G.S., Luebker, A., 1987. Psychophysics of reading: V. The role of
contrast in normal vision. Vision Research 27 (7), 1165–1177.
Legge, G.E., Parish, D.H., Luebker, A., Wurm, L.H., 1990. Psychophysics of reading:
XI. Comparing color contrast and luminance contrast. Journal of Optical Society of
America A 7 (10), 2002–2010.
Mary C. Dyson, 2004. How physical text layout affects reading from screen. Behaviour
& Information Technology. Nov.-Dec. 2004, VOL. 23, NO. 6, 377–393.
Marie-Catherine Beuscart-Zéphira et al, 2010. Example of a Human Factors
Engineering approach to a medication administration work system: Potential impact on
patient safety. International Journal of Medical Informatics, 2010, 79, e43–e57.
Mohamed Zaki Ramadan, Evaluating college students’ performance of Arabic typeface
style, font size, page layout and foreground/ background color combinations of e-book
materials
Nathalie Bonnardel et al., The impact of color on Website appeal and user cognitive
processes, Displays 32 (2011) 69-80.
Pace, B.J., 1984. Color combinations and contrast reversals on visual display units. In:
Proceedings of the Human Factors Society 28th Annual Meeting. HFES, Santa Monica,
CA, pp. 326–330.
32
Pastoor, S., 1990, Legibility and subjective preference for color combinations in text.
Human Factors 32 (2), 157–171.
Pearson, R., van Schaik, P., 2003, The effect of spatial layout of and link color in Web
pages on performance in a visual search task and an interactive search task.
International Journal on Human–Computer Studies 59, 327–353.
Radl, G.W., 1980, Experimental investigations for optimal presentation-mode and
colors of symbols on the CRT-screen. In: Grandjean, E., Vigliani, E. (Eds.), Ergonomic
Aspects of Visual Display Terminals. Taylor & Francis, London, UK, pp. 127–135.
Rahmah Lob Yussof et al., Affective engineering of background color in digital
storytelling for remedial students, Social and Behavioral Sciences 68 (2012) 202-212.
Shieh, K.-K., Chen, M.-T., 1997. Effects of screen color combination, work-break
schedule, and workplace on VDT viewing distance. International Journal of Industrial
Ergonomics 20 (1), 11–18.
Shieh, K.-K., Lin, C.-C., 2000. Effects of screen type, ambient illumination, and color
combination on VDT visual performance and subjective preference. International
Journal of Industrial Ergonomics 26 (5), 527–536.
Shieh, K.-K., Chen, M.-T., Chuang, J.-H., 1997, Effects of color combination and
typograghy on identification of characters briefly presented on VDTs. International
Journal of Human–Computer Interaction 9 (2), 169–181.
33
Small, P.L., 1982. Factors influencing the legibility of text/background color
combinations on the IBM 3279 color display station. Report No. HF066, IBM Hursley
Park, Winchester, England.
Spenkelink, G.P.J., Besuijen, J., 1996, Chromaticity contrast, luminance contrast, and
legibility of text. Journal of the Society for Information Display 4 (3), 135–144.
Tinker, A.M., Paterson, G.D., 1928. Influence of type form on speed of reading. Journal
of Applied Psychology 12, 359–368.
Tinker, A.M., Paterson, G.D., 1929a. Studies of typographical factors influencing speed
of reading: II. Size of type. Journal of Applied Psychology 13 (2), 120–130.
Tinker, A.M., Paterson, G.D., 1929b. Studies of typographical factors influencing speed
of reading. III. Length of line. The Journal of Applied Psychology 13 (3), 205–219.
Tinker, A.M., Paterson, G.D., 1929c. Studies of typographical factors influencing speed
of reading: V. Simultaneous variations of type site and line length. The Journal of
Applied Psychology 13 (2), 72–78.
Tinker, A.M., Paterson, G.D., 1929d. Studies of typographical factors influencing speed
of reading: VI. Black type versus white type. The Journal of Applied Psychology 13
(2), 241–247.
34
Tinker, A.M., Paterson, G.D., 1929e. Studies of typographical factors influencing speed
of reading. VII. Variations in color of print and background. Journal of Applied
Psychology 13 (2), 471–479.
Tinker, A.M., Paterson, G.D., 1932. Studies of typographical factors influencing speed
of reading: X. Style of typeface. The Journal of Applied Psychology 16, 605–613.
Tinker, A.M., Paterson, G.D., 1940. How to Make Type Readable: A Manual for
Typographers, Printers, and Advertisers, Based on Twelve Years of Research Involving
Speed of Reading Tests Given to 33,031 Persons. Harper & Brothers, New York.
Tinker, A.M., Paterson, G.D., 1942. Reader preferences and typography. Journal of
Applied Psychology 26, 38–40.
Tinker, A.M., Paterson, G.D., 1944. Eye movements in reading black print on white
background and red print on dark green background. American Journal of Psychology
57, 93–94.
Tinker, A.M., Paterson, G.D., 1946. Readability of mixed type forms. Journal of
Applied Psychology 30, 631–637.
Tinker, A.M., 1955. Tinker Speed of Reading Test. University of Minnesota Press,
Minneapolis.
Tinker, A.M., 1963. Legibility of Print. Iowa State University Press, Ames, IA.
35
Wu, J.H., Yuan, Y.F., 2003. Improving searching and reading performance: the effect
of highlighting and text color coding. Information & Management 40 (7), 617–637.
Xue-min Zhang, Hua Shu, Tian Ran, Effect of computer screen back and font color on
Chinese reading comprehension, 2007 International Conference on Intelligent
Pervasive Computing.
Yi-Jan Yau, Chin-Jung Chao, Sheue-Ling Hwang, Optimization of Chinese interface
design in motion environments, Displays 29 (2008) 308-315.
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