Reexamination of Color 

Vision Standards, Part II. 

A Computational Method 

to Assess the Effect of 

Color Deficiencies in 

Using ATC Displays

Jing Xing 

David J. Schroeder

Civil Aerospace Medical Institute

Federal Aviation Administration

Oklahoma City, OK 73125

March 2006

Final Report

DOT/FAA/AM-06/6

Office of Aerospace Medicine

Washington, DC 20591
NOTICE

This document is disseminated under the sponsorship 

of the U.S. Department of Transportation in the interest 

of information exchange. The United States Government 

assumes no liability for the contents thereof.

___________

This publication and all Office of Aerospace Medicine 

technical reports are available in full-text from the Civil 

Aerospace Medical Institute’s publications Web site: 

www.faa.gov/library/reports/medical/oamtechreports/index.cfm
Technical Report Documentation Page 

1. Report No. 2. Government Accession No. 3. Recipient's Catalog No. 

DOT/FAA/AM-06/6 

4. Title and Subtitle 5. Report Date 

Reexamination of Color Vision Standards, Part II. A Computational March 2006 

Method to Assess the Effect of Color Deficiencies in Using ATC 

Displays 

6. Performing Organization Code 

 

7. Author(s) 8. Performing Organization Report No. 

Xing J, Schroeder DJ 

9. Performing Organization Name and Address 10. Work Unit No. (TRAIS) 

FAA Civil Aerospace Medical Institute 

P.O. Box 25082 11. Contract or Grant No. 

Oklahoma City, OK 73125 

12. Sponsoring Agency name and Address 13. Type of Report and Period Covered 

Office of Aerospace Medicine 

Federal Aviation Administration 

800 Independence Ave., S.W. 

Washington, DC 20591 14. Sponsoring Agency Code 

15. Supplemental Notes 

Work was accomplished under approved task AM-HRRD522. 

16. Abstract 

The previous study showed that many colors were used in air traffic control displays. We also found that colors 

were used mainly for three purposes: capturing controllers’ immediate attention, identifying targets, and 

segmenting information. This report is a continuing effort to reexamine the FAA’s color vision standards, 

focused on understanding how individuals with color vision deficiencies use color-coded information. We first 

reviewed and synthesized the literature about the effectiveness of color relative to achromatic visual cues. Next, 

we developed several algorithms to assess the effects of color for individuals with color vision deficiencies. Using a 

computational algorithm that simulates how color deficient individuals perceive color, we were able to calculate 

the effectiveness of color in task performance. By considering together the effectiveness of redundant visual cues 

and the perception of those with color vision deficiencies, we provide a method to assess the potential effects of 

color deficiencies in using color displays. 

 

17. Key Words 18. Distribution Statement 

 

 

Air Traffic Control, Displays, Color Vision Deficiency, 

Design, Redundant Cue

Document is available to the public through the 

Defense Technical Information Center, Ft. Belvior, VA 

22060; and the National Technical Information 

Service, Springfield, VA 22161 

19. Security Classif. (of this report) 20. Security Classif. (of this page) 21. No. of Pages 22. Price 

Unclassified Unclassified 18 

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized

iii

ACKNOWLEDGMENTS

We thank Drs. Carol Manning and Larry Bailey for their valuable comments on the manuscript. 

We also express our appreciation of the support from Mr. Dino Piccione and Stephen Cooley. We 

thank Janine King for her enormous help in editing an early version of the manuscript. We appreciate 

the FAA reviewers and editors whose efforts greatly improved this report.

Reexamination of Color Vision Standards, Part II. 

A Computational Method to Assess the Effect

of Color Deficiencies in Using ATC Displays

INTRODUCTION

People with normal color vision have three types of 

cone receptors in their retinas to perceive color. Each type 

of cone receptors contains a photopigment that responds 

to a restricted range of the light spectrum corresponding 

to red, green, or blue. Color deficient individual (CD) is a 

term denoted to a person who has abnormal or incomplete 

cone receptors. Compared to color-normal individuals 

(CN), CDs perceive a reduced range of color because of 

the absence of some types of cones. Roughly 8-10% of 

all males are CDs, while very few females are CDs. There 

are three types of CDs. 1) Anomalous trichromats have 

all three types of cones but one cone type is rare. Such 

individuals can see all color categories but have difficulty 

in discriminating color that a normal vision person can 

easily distinguish. 2) Dichromats have only two primary 

cones; one type is missing. Dichromats are further classified into three types: protanope, deuteranope, and 

tritanope. Protanopes and deuteranopes constitute the 

majority of CD populations. They have red-green color 

deficiencies and see only yellow and blue. The tritanope is 

analogously blue-yellow color deficient. 3) Monochromats 

have no cones and therefore no color vision at all. They 

are very rare, about one in ten million people. 

Given that color displays are being widely used in 

many professions including air traffic control (ATC), 

it is important to understand how CDs perceive colors. 

Brettel, Vienot, and Mollon proposed an algorithm to 

simulate for normal observers the appearance of a color 

image for individuals with dichromatic types of color 

deficiencies (Brettel, Vienot, & Mollon, 1997; Vienot, 

Brettel, & Mollon, 1999). Inspired by Brettel’s work, 

Dougherty developed a computer program, called Vischeck, to simulate the entire color process of human vision 

(see www.vischeck.com for reference). The Vischeck model 

can be divided into three stages. The first stage includes 

the physical properties of display devices, ambient lighting, and physiological factors. The second stage describes 

the transformation of an optical image on the retina into 

a neural representation of that image. This stage used 

Brettel’s algorithm to model the effects of dichromats. The 

final stage is a model of human cortical vision. This stage 

includes mechanisms in which color, spatial patterns, and 

motion are combined and processed in the visual cortex 

to form the observer’s perception of the image. Figure 1 

shows a simulation sample obtained from the Vischeck

program. The figure presents the images seen by deuertanopes and protanopes after transformation of each pixel 

of the original image. Figure 1 indicates that most colors 

appear darker and less saturated for CDs. For example, red 

and green appear dark yellowish. Overall, CDs generally 

Figure 1: An example of colordeficit simulation produced by 

the Visckeck program. The top 

image shows some colorful hats 

seen by color-normal observers; 

the middle is the image as seen 

by a person with deuteranopia; 

the bottom is the image as seen 

by a person with protanopia.
can see colors to a certain extent, but they have trouble 

distinguishing between some shades of red and green 

or blue and yellow. They also cannot differentiate some 

colors that are perceptually close to each other.

Our previous study (Xing & Schroeder, 2005) demonstrated that the color vision standards currently used 

by the FAA have allowed certain types of CDs to pass the 

practical color vision screening tests for air traffic control 

specialists. However, in the past five years there has been 

an increased use of color in displays being introduced in 

ATC facilities. Controllers with color vision deficiencies 

may experience difficulties in using color-coded information appropriately, because color deficiency was not 

a consideration at the ATC display design.

Simulation programs such as Vischeck provide a means 

to explore how the ATC displays would appear to CDs. 

However, not only do we need to know how colors appear to CDs, but we also need to determine how reduced 

color perception affects task performance. Our previous 

study has shown that colors on ATC displays are used 

for three purposes: 1) to capture attention; salient colors 

are often chosen to encode information that needs to be 

attended to immediately, such as an alert or emergency; 

2) to identify categories of information so that searching 

for specific information in a complex image can be done 

more effectively; in this case, each color is associated with 

a distinctive meaning; 3) to segment complex images in a 

display into distinctive groups so that information belonging to the same category can be organized together. In 

this application, color does not have inherent meanings. 

Therefore, we use three terms to refer to color-related 

tasks throughout this report: attention, identification, and

segmentation. We synthesized the literature for studies on 

the use of color in these types of tasks. Using the experimental data in the literature as criteria, we were able to 

assess the potential effects of color use for CDs.

Our previous observations of ATC displays revealed 

that color was often used along with achromatic redundant cues (ibid). Presumably, redundant cues would 

allow CDs to perform the tasks. However, redundant 

cues may not be as effective as colors in supporting task 

performance. Therefore, when assessing the effects of 

color vision deficiencies on task performance, we have to 

consider the effectiveness of redundant cues, together with 

the reduced color perception of CDs. In this report, we 

analyzed the effects of achromatic cues relative to colors, 

and we developed a set of computational algorithms to 

compare the effectiveness of colors for CDs versus CNs. 

Furthermore, we applied the algorithms to the general 

use of color in ATC displays. The results are presented in 

several look-up tables that can easily be used to assess the 

effects of color vision deficiencies for a given display. 

This report has two major parts: 1) rationales for 

the effectiveness of color based on research data in the 

literature; and 2) algorithms we developed to assess the 

effects of colors for CDs. We also discuss the potential 

applications and limitations of the algorithms in evaluating ATC color displays with respect to CDs.

RESULTS

Rationale for the usefulness of color

This section synthesizes the research literature to 

provide a rationale for the effectiveness of color in displays. Such a rationale is the basis for the algorithms 

described in the following section. Given that colors in 

ATC displays are used mainly for three types of tasks: 

Attention, Identification, and Segmentation, this section 

is focused on visual studies in which subjects performed 

tasks involving those activities. In addition, we also reviewed studies of text readability associated with colors. 

Reading text from displays is an important part of ATC 

tasks. Even though the use of colors in a display is not 

designed to influence text readability, applying color to 

text nevertheless affects readability. 

Color can be specified by two factors: luminance and 

chromaticity (hue). Our literature review was intended to 

elucidate the relationship between the effects of color use 

and color specifications. In particular, we sought to determine the threshold point for color specifications, below 

which color is no longer effective for a given purpose, and 

the saturation point, beyond which the effectiveness of 

color does not increase further. Since these critical points 

might vary with experimental conditions, we relied more 

on studies conducted using complex images on large 

displays (mimicking the situations of ATC). 

Introduction to color systems 

A color system refers to the mathematical description 

of colors. A full description of color includes two factors: 

chromaticity (hue) and luminance (brightness). There 

are many kinds of color systems developed for different 

purposes. Here, we briefly introduced several systems 

used in this report. 

As mentioned earlier, human retinas have three types 

of cones that are sensitive to colors. Each type is sensitive 

to a special region of the light spectrum (Long, Medium, 

and Short). Thus, a cone is referred to as L-, M-, or Scone, each for its sensitive region of the light spectrum. 

An L-cone has its peak sensitivity to red; an M-cone is 

most sensitive to green, and an S-cone spreads over the 

blue region of the light spectrum. The excitation of each 

type is denoted with L, M, and S. Therefore, a color can 

be specified with these three values of cone excitation. 
The proportion of stimulated L-, M-, and S-cones 

determines the chromaticity of a color, while the luminance of a color is the sum of excitation of all cones, i.e., 

(L+M+S). This system is best suitable to describe color 

deficiencies because certain types of cones are absent in 

CDs (Wandell, 1995).

While the LMS systems describe the activity in the 

retinas induced by a color, its variables cannot be measured directly. On the other hand, The International 

Committee of Illumination (CIE) defined color chromaticity coordinates to describe color perception. In this 

definition, a color can be specified by three parameters: 

L, x, and y, where L is the luminance of a color and x 

and y determine the chromaticity. A colorimeter can 

measure the Lxy values of a surface. Because of its ease 

of measurement and independence from observers, 

CIE chromaticity is the most widely used color system. 

However, one of the greatest disadvantages of the CIE 

chromaticity systems is that visually they are not equally 

spaced. Thus, distortions occur in attempting to relate 

perceived colors to locations of the CIE chromaticity 

diagram. Based on the Lxy systems, the CIE adopted 

the Lu ́v ́ systems that were more nearly uniformly 

spaced with respect to color perception. Therefore, 

the chromaticity difference between two colors can be 

computed as ((∆u ́) 2 + (∆v ́) 2)

1⁄2. The values of u ́ and 

v ́ can be computed from x and y through two non-linear 

equations. 

A computer display generates a color through three 

phosphor channels: red, green, and blue. The amount 

of phosphors emitted from each channel is specified 

with the digital value of the channel: r, g, or b, each for 

red, green, or blue phosphors. Computer programmers 

use these numbers to specify a color on displays. For 

example, rgb values of (255, 0, 0) are for red and (255, 

255, 0) are for yellow. The relationship between the rgb

and Lxy systems can be easily specified with a nonlinear 

transformation and a linear matrix transformation. The 

process of determining the transformations is called color 

calibration.

Besides the above color systems, there are many other 

mathematic descriptions of color. The good news is that 

these color systems can be transformed from one to another 

by a set of mathematical equations. In this report, we used 

the CIE uniform chromaticity systems (Lu ́v ́) to describe 

color differences. While the results in the literature were 

expressed in different systems, we converted those data 

into Lu ́v ́ specifications. Since the transformation from 

rgb values to other color systems requires calibration of 

the monitor, we adopted the default monitor calibration 

parameters used in the Vischeck program, as shown in 

the Appendix A.

Attention

Critical information presented on ATC displays needs 

to be detected immediately without serial searching 

through a display. Therefore, the target should become 

obvious immediately to capture an observer’s attention. 

This phenomenon is called “pop-out.” Treisman and 

Gelade (1980) found that a number of visual attributes 

could induce pop-out and by doing so allow information to be extracted in parallel across a large visual field. 

By parallel, we mean that the amount of visual stimuli 

on a display has little effect on the time and accuracy of 

target detection.

Pop-out of color-coded information is extremely efficient and desirable at display design (Treisman & Gelade, 

1980). Colors help when targets need to be detected in 

large displays. By popping out, targets located in the 

peripheral visual field can be brought quickly to the fovea 

for detailed inspection. Christ (1975) also confirmed 

that introducing colors on large displays helps detection 

because information could be processed in parallel. 

Many studies have been devoted to the effectiveness 

of visual attributes in drawing attention. Christ (1975) 

compared the data in the literature and concluded that 

color was the most effective cue to draw attention in 

complex scenes, followed by luminance and shape. On 

the other hand, Banks and Weimer (1992) examined 

subjective evaluations using visual features to highlight 

alert messages. Their results indicated that flashing was 

ranked as the top choice, followed by brightness (luminance), color, and then attributes such as size and shape. 

While flashing is most effective in drawing attention, its 

use should be limited because it produces tunnel vision, 

in which the observer focuses on the target and ignores 

other information in the visual field. 

A number of studies have demonstrated that visual 

conspicuity draws attention instantly. For static visual 

stimuli, the conspicuity of a target is mainly determined 

by two factors: color and luminance differences between 

the target and other stimuli in the visual field (referred 

to as distractors). Johnson, Liao, and Granada (2002) 

reported that visual conspicuity of aircraft symbols 

increased linearly with the target luminance. Their 

results suggested that there was no saturation point for 

the effectiveness of luminance in drawing attention. 

Wyszecki and Fielder (1971a) reported that the threshold luminance difference for Attention is about 75 times 

the threshold at which the target and distractors could 

be discriminated. Nagy and Sanchez (1992) found that 

the threshold luminance difference varied with the size 

of displays and stimuli. They demonstrated that, if the 

luminance difference between the target and distractors 

was larger than ~10 cd/cm2 on a small display field and 

~20 cd/ cm2 for small stimuli (~1.5cm, 0.5 degree) on 


a large display field, observers could detect the target 

instantly without searching among distractors. They 

also showed that there was no advantage for combining 

a luminance difference with a chromaticity difference 

when the target was brighter than the distractors. On 

the other hand, a target dimmer than distractors could 

hardly capture attention. In that situation, an additional 

chromaticity difference might be helpful. 

Much effort has been made to determine the threshold 

chromaticity difference between a target and distractors 

in attention capture. Carter and Carter (1981) concluded 

that the difference should be at least 7~13 times the 

color-discrimination threshold (the one determined by 

Wyszecki & Fielder, 1971a,b). Nagy and his colleagues 

performed a series of studies to determine the threshold 

difference by varying display size, stimulus size, and 

stimulus location in the visual field (Nagy & Sanchez, 

1990; Nagy, Sanchez, & Hughes, 1990). The situation 

that was most similar to ATC displays was the combination of small stimuli (~1.5cm, 0.5 degree) and large 

display fields. Under these conditions, they estimated 

that the threshold chromaticity difference is about 20~60 

times the color discrimination threshold. Given that the 

Wyszecki-Fielder threshold is typically taken as 0.004

in the CIE uniform chromaticity systems, 60 times that 

threshold would be 0.24. 

Identification

Color is often used denotatively to identify an object. 

Tasks using color for identification essentially involve 

color naming, in which observers can associate targets 

with specific color names in their mental processing. For 

example, colors may be used to identify different aircraft 

on ATC displays. In real life, identification of two stimuli 

is usually performed at separate spatial locations and 

times. Typically, an observer remembers the color by its 

name and then identifies the target by the color name. 

Color has been shown to be the most effective visual 

cue for identification tasks (Young & Nagy, 2003). On 

the other hand, several studies have demonstrated that 

labeling information with different levels of luminance 

did not help much in tasks involving quickly identifying 

targets, especially when memory was required (Sachtler 

& Zaidi, 1992). 

There exist some controversial debates about the effects of color in identification tasks. Color and shapes 

are processed independently in the brain. When both 

codes are present, color is generally dealt with first, which 

makes sense in a search task but may not be the optimum 

strategy when the target location is known. Therefore, it 

was not a surprise that some studies suggested that color 

has little advantage in identification. For example, Luder 

and Barber (1984) asked subjects to identify the status 

of a known location on an aircraft cockpit display unit. 

Their results demonstrated that color-coding was not 

more helpful than achromatic coding. Also, Kahneman 

and Treisman (1984) found that attending to a red-colored letter only facilitated attention to redness but not to 

the meaning of the letter. Thus, they claimed that color 

helped search tasks but not identification.

Nevertheless, the majority of studies have demonstrated 

that color is the most effective attribute for labeling 

information in visual displays. In particular, for large 

displays with complex images, results in the literature 

are generally consistent about the superior role of color 

to achromatic cues (Christ, 1975). In a large display, a 

colored target can be found more quickly than achromatic targets, and the performance difference increases 

as the display size increases. When used to identify information such as aircraft shapes, geometric shapes, and 

alphanumeric signs, colors were significantly better (in 

terms of accuracy) than size, brightness, shape, and text. 

Christ further indicated that the identification of colors 

became increasingly superior to achromatic features as 

the difficulty of identifying achromatic features increased. 

Moreover, colors became increasingly more effective as 

recall was delayed. 

Little research has been devoted to determining the 

threshold and saturation points of the effectiveness of color 

for identification. However, considerable data have been 

accumulated about color naming. Given that a task of 

using color for identification is essentially the task of color 

naming, we could determine the critical points based on 

color-naming studies. Berlin and Kay (1969) suggested 

that there might be only 11 categories of basic colors; 

each associated with a well-learned name and possibly 

unique physiological substrates. The 11 categories were: 

red, green, yellow, blue, purple, brown, orange, pink, and 

three achromatic names: black, white, and gray. Boynton 

and Olson (1990) confirmed this contention. They found 

that basic colors were maximally segregated in the color 

space. Boynton and Olson further showed that basic 

colors were superior to other colors in identification tasks 

because targets were identified more quickly and reliably. 

However, Smallman and Boynton (1990) found that the 

efficiency of color in identification is largely determined 

by color differences. They demonstrated that a set of nonbasic colors with the same color differences as the basic 

colors had about the same effect in identifying targets, 

even though basic colors still had some superiority when 

the identification tasks involved complex scenes. 

The threshold point for color in identification can 

be inferred from the color-naming threshold, which 

has been well examined by Boynton and his colleagues. 

Boynton, MacLaury, and Uchikawa (1989) reported that 

the color-naming threshold was about nine times the 
color discrimination threshold. In a different approach, 

Poirson and Wandell (1993) found that the threshold for 

identifying a target by the color name is about seven times 

the color discrimination threshold. Poirson and Wandell 

asked subjects to detect a color target in a set of briefly 

presented objects. This kind of identification task was 

easier because subjects were asked only to identify one 

color at a time, while air traffic controllers often need to 

associate several colors with different types of information in ATC displays. Thus, the threshold determined 

by Boynton et al. (1989) is more appropriate to assess 

the effectiveness of color in ATC displays. We took nine 

times the discrimination threshold as the critical threshold 

point for identification tasks, which is 9×0.004=0.036. On 

the other hand, the differences between basic colors are 

at least 40 times the discrimination threshold; therefore, 

we took the color difference of 40x0.004 = 0.16 as the 

saturation point for Identification tasks. 

Segmentation

When viewing a complex scene, the human visual 

system first organizes the scene into meaningful objects. To appreciate what and where particular objects 

are present, the visual input is organized by a filtering 

procedure that has been termed segmentation (Pinker, 

1984). Segmentation becomes crucial when dealing with 

an automation system that is usually characterized by a 

cluttered display and varying task demands. Since color 

is processed separately from achromatic features by the 

visual system, it is one of the ways to segment a display 

into separate regions.

Segmentation is based on uniformity and consistency. 

An area composed of uniform elements can be segmented 

easily from its surround. Hence, in a real display, color 

is usually more effective than form cues for segmentation because form cues are used with explicit meaning. 

Nothdurft (1993) compared the effectiveness of visual 

cues that involved texture segmentation. He reported that, 

while color and luminance differences were both effective 

in producing regional segmentation and figure-ground 

organization, color information is dealt with first before 

achromatic cues. On the other hand, the experiment 

performed by Yamagishi and Melara (2001) indicated 

that luminance information was more effective than color 

to extract boundary representations, while chromaticity 

information is more effective in regional segmentation. 

In the ideal situation, where the objects are homogeneous visual stimuli, the threshold color differences 

for segmentation are the same as the discrimination 

thresholds. However, completely homogeneous stimuli 

carry little information. That is a rare situation in ATC 

displays, which are typically composed of various text 

and symbols. Thus, we should review the literature in 

texture segmentation for the threshold points. 

The luminance discrimination threshold has been 

well studied. The ratio between the threshold and the 

baseline luminance of an object is a constant for most 

of the luminance range produced by a display. The ratio 

is about 0.05 for uniform stimulus areas placed side-byside. McIlhagga, Hine, Cole, and Snyder (1990) reported 

that the ratio for texture fields is about 0.15~0.2. Two 

texture regions with a luminance difference above the 

threshold ratio can be reliably segmented. Hence, we 

took 0.2 as the critical luminance threshold point for 

segmentation. As for color, the typical color discrimination threshold is about 0.004 for uniform stimuli placed 

side-by-side. The threshold increases as the stimulus areas 

become less uniform. Industries typically use three times 

the standard color discrimination threshold for texture 

discrimination. We thus took 3×0.004 as the threshold 

point of chromaticity difference for segmentation. There 

is little data about the saturation point for segmentation 

in the literature. However, it is reasonable to assume that 

the effectiveness of color in segmentation increases with 

chromaticity difference.

Text readability

ATC tasks involve considerable text reading because 

text comprises a relatively large part of the materials on 

displays. Text readability is defined as the property that 

permits an observer to read text easily on a screen irrespective of their meanings. In visual displays, readability 

is measured as the time required to find and read given 

text, or the number of words read per minute. Many 

experiments have demonstrated that text readability is 

predominately determined by the luminance contrast 

between the text and its background. Luminance contrast 

of text can be defined in several ways. Among those, 

Michelson’s contrast definition is most widely used. It 

is defined as follows:

C=(Lt-Lb) / (Lt+Lb)

Where Lt is text luminance and Lb is background luminance. The contrast C varies between 0 and 1. 

The effect of color in text reading is not as clear as 

that of luminance. Knoblauch, Arditi, and Szlyk (1991) 

examined the role of color in text reading. They found 

that, when luminance contrast is greater than 0.1, reading 

performance was unaffected by the presence of chromatic 

contrast over a range of character sizes varying 30-fold; 

only when luminance contrast was reduced to near the 

threshold for reading did chromatic contrast sustain reading. Ojanpaa and Näsänen (2003) measured the reading 

rate at different luminance contrasts while keeping the 
color contrast constant. Interestingly, the results showed 

that reading rate decreased strongly when the luminance 

contrast approached zero. Thus moderate color contrast 

was not sufficient for effective visual search or reading 

when the luminance contrast was small. These findings 

may be because at high spatial frequencies, such as text, 

contrast sensitivity for pure color information is considerably lower than for luminance information.

Other studies seem to suggest a stronger role of color 

in reading. Pastor (1990) evaluated displayed colors by 

having subjects rank text readability. The results showed 

that color saturation had the most important influence 

on ratings. A similar experiment performed by Van Nes 

(1986) also demonstrated that white, yellow, cyan, and 

green yielded higher rating scores than magenta, blue, 

and red. In addition, Cowan and Ware (1987) reported 

that high brightness colors received high rankings for 

readability. However, all these results can be explained 

by the fact that the colors yielding great readability were 

the ones with high luminance. 

Legge and his colleagues have performed a series of 

quantitative studies on text readability (Legge, Parish, 

Luebker, & Wurm, 1990). They measured reading speed as 

a function of luminance contrast, color contrast, or both. 

They found that reading speed decreased by approximately 

a factor of two when text contrast (Michelson contrast) 

decreased from 100% to 20%. This result implied that 

there was no saturation point in the effect of luminance 

contrast on reading. However, below 20% reading speed 

slowed much more rapidly and was significantly below 

the normal reading speed. Thus, a text contrast of 20% 

can be referred to as the threshold point for reading. 

This threshold choice is supported by other studies. For 

example, Scharff and Ahumada (2002) measured text 

readability as the time needed to search and read given 

text. The results indicated that text readability increased 

with contrast and deteriorated significantly for contrasts 

below 20~30%. In addition, Travis, Bowles, Seton, and 

Peppe (1990) suggested that luminance contrast should 

be about 50% for text reading on displays, to stay away 

from the threshold contrast for reading. 

Redundant cues 

When achromatic visual features are used in addition 

to colors to code information, those features are considered as redundant cues. Christ (1975) defined redundant 

cue as follows: “Target attributes were considered to be 

redundant if the target could be identified either in terms 

of colors or achromatic visual features.” By this definition, the color and redundant cues of a target should be 

perfectly correlated. Target attributes were considered to 

be non-redundant if the targets could be identified or 

located only in terms of their color or achromatic features. 

If a task was to identify one attribute as the critical target 

feature and allowed one or more attributes to vary uncorrelatedly, that was also non-redundant.

Color and redundant cues may have different 

efficiencies in various color-related tasks. For example, 

a flashing signal is more effective than colors in drawing 

attention because the majority of the visual field is more 

sensitive to dynamic signals than to static ones. Among 

static visual features, while colors have been shown to 

be the most effective cue for attention and target search 

(Young & Nagy, 2003), a number of achromatic visual 

features, such as luminance, shape, and texture orientation, can also produce pop-out (Treisman & Souther, 

1985). Another cue is the spatial location, provided that 

the information always appears at the same location, and 

one can easily remember it. Yantis and Jonides (1996) 

have demonstrated that sudden onset of a target at a 

known location captured attention. 

Christ (1975) summarized the data in the literature 

about the effectiveness of colors relative to achromatic 

attributes. He calculated a difference score, which was the 

difference between performance with color in displays and 

without color in displays divided by the results obtained 

without colors. The maximum scores reported in the 

literature for Identification tasks were 202% for geometric 

shape, 176% for size, 62% for other shapes, 46% letters, 

and 19% for digits. The scores for Attention tasks were 

53% for geometric shapes and 69% for other shapes. The 

positive values of these scores indicated that color was the 

most effective cue in both types of tasks. The effectiveness 

of achromatic cues relative to each other could also be 

inferred from the differences in the scores. 

Algorithms to assess the effectiveness of colorcoding and achromatic redundant cues for CDs

The goal of this section was to develop a computational 

method to assess how CDs use color-coded information 

relative to CNs. We approached the goal through the 

following steps:

1. Assess the effectiveness of color for CDs relative to 

CNs. This could be done by combining a dichromate 

simulation program and the experimental results of 

effectiveness of color reviewed in the previous section. 

While the effectiveness of color might vary continuously with color parameters in most situations, we 

classified the results of comparisons into three levels: 

“E” referred to situations where color was equal to or 

more effective for CDs than for CNs; “L” represented 

situations where color was less effective for CDs than 

for CNs; and “NE” meant that color was not effective 

for CDs. In addition, we used “NA” to denote the “not 

applicable” situation where color was not even effective 

for CNs. No comparison of color effects between CDs 
and CNs was made for “NA” situations. 

2. Assess the effectiveness of achromatic redundant cues. 

We also used a three-level scale, E, L, and NE, to classify 

the effects of color relative to achromatic cues. Once 

again, “NA” represented the situation where color was 

not effective for CDs. 

3. The usefulness of color for CDs depended on both 

types of effectiveness. The overall usefulness was 

determined by a “winner-takes-all” rule, i.e., by the 

higher level of the two. For example, if the effect of 

color is “L” while the effect of redundant cues is “E,” 

the overall effect is “E.” It means that CDs, with the 

aid of redundant cues, could perform the color-related 

task equally well as did CNs. 

We could use either the algorithm developed by Brettel 

et al. (1997) or Dougherty’s Vischeck program to compute the perceived color for CDs. The two methods are 

equivalent for simple color stimuli. For complex scenes, 

the Vischeck program simulates color perception more 

accurately because it takes spatial interaction of color 

into consideration. Both methods allow simulations of 

deuteranopia, protanopia, and tritanopia. Since tritanopia 

is rare, we do not include this type in our analysis below. 

We analyzed the effects of color for deuteranopes and 

protanopes, respectively, and then combined the effects as 

the final assessment for CDs. The combination rule was 

opposite of “winner-takes-all,” i.e., the final effectiveness 

of color for CDs was determined by the lower value of 

the effectiveness for deuteranopes and protanopes. 

Attention 

The effects of color in drawing attention depend on 

the visual conspicuity of the target, which, in turn, depends on the color and luminance differences between 

the target and distractors. Most visual displays have 

a dark background, so conspicuity of a target usually 

increases with the luminance of the target. According to 

our literature review, there is no saturation point in the 

relationship between conspicuity and the effectiveness 

of color in drawing attention. The threshold luminance 

difference for attention is 20cd/cm2. The threshold 

chromaticity difference is 0.24 in the CIE uniform 

chromaticity systems. 

We computed the effects of color on Attention using 

the following steps:

1. Convert the images from a color display into the images that would be perceived by CDs; 

2. Determine chromaticity values (Lxy) for target, distractors, and background. This could be done either by 

colorimeter measurements or by formula transformation from rgb values on the display;

3. Compute color and luminance differences between 

the target and background, denoted as ∆Ctb and 

∆Ltb; color difference is computed as ∆Ctb = ((∆u ́)2

+ (∆v ́)2)

1⁄2

;

4. Compute color and luminance differences between the 

target and distractors, denoted as ∆Ctd and ∆Ltd;

5. Compute color and luminance differences between 

the target and distractors / background for CNs; 

6. Compare the differences with the threshold and 

saturation points to determine the effects of color for 

CDs: 

If ∆Ctb < 0.24 and ∆Ltb < 20 for CNs, then the 

effect is “NA”

If ∆Ctd < 0.24 and ∆Ltd < 20 for CNs, then the 

effect is “NA”

If ∆Ctb < 0.24 and ∆Ltb < 20 for CDs, then the 

effect is “NE”

If ∆Ctd < 0.24 and ∆Ltd < 20 for CDs, then the 

effect is “NE”

Else,

If both ∆Ltb and ∆Ltd for CDs are equal to or greater 

than those for CNs, then the effect is “E”

If ∆Ctb and ∆Ctd for CDs are equal to or greater 

than those for CNs, then the effect is “E”

If ∆Ltb or ∆Ltd is less for CDs than for CNs, then 

the effect is “L”

If ∆Ctb or ∆Ctd is less for CDs than for CNs, then 

the effect is “L”

We applied the above procedure to ATC displays. While 

an ATC display may have many colors and luminance, 

it should have one or two default colors with which the 

majority of materials are displayed, and they are not considered as color-coding. In primary ATC displays such as 

Display System Replacement (DSR), the default colors 

are white, green, or yellow-green. The background color 

is typically black or dark blue. One problem with ATC 

displays is that controllers can adjust screen brightness 

to their own preferences, and sometimes they can adjust 

the luminance of different types of displayed messages 

individually. We mimicked these two situations by a) having the target and distractors all in the 100% luminance 

of the given color, mimicking that controllers adjust the 

overall brightness of the screen; and b) having the target 

in 100% luminance and distractors in 50% luminance, 

mimicking that controllers adjust the brightness of the 

default color (such as that of datablocks) but not the 

overall brightness. 

We computed the effectiveness of some target colors 

for Attention. The computation was made for the ten 

colors that are frequently used to draw attention in ATC 

displays: red, green, yellow, blue, orange, brown, pink, 
purple, cyan, and magenta. The background color was 

20% gray. The distractor color was white, green, or yellow-green in 100% or 50% brightness. The results are 

shown in Table 1, with the rows specifying target color and 

the columns specifying distractor color. The comparison 

between the effects of color for CDs and for CNs are 

represented in the three scales stated earlier: “E” means 

equally effective for both CDs and CNs, “L” means “less 

effective” compared with CNs, and “NE” means “not 

effective” for CDs. The majority of data in Table 1 are 

“NE,” suggesting that color is not an effective attention 

attribute for CDs. 

Identification 

The effects of color for Identification depends on the 

chromaticity differences between colors and how well 

colors can be named. Since luminance is not an effective 

cue for Identification, we only need to compute chromaticity differences between colors that are used to identify 

different categories of information and the differences 

between target colors and the background. The critical 

points of color difference are 0.036 for threshold and 0.16 

for saturation. We computed the effectiveness of color for 

Identification by CDs using the following steps:

1. Convert the images from a color display into the images that would be perceived by CDs; 

2. Determine chromaticity values for the background 

color and the colors that are used to identify information for CDs;

3. Determine chromaticity values for the background 

color and the colors that are used to identify information for CNs;

4. Compute the color difference ∆C for each pair of 

colors for CDs and color difference ∆C0 for CNs. 

5. Compare the color differences with critical points to 

determine the effects of color for CDs: 

If ∆C0 < 0.036, then the effect is “NA”

For the situations where ∆C 0>=0.08,

If ∆C <0.036, then the effect is “NE”

If ∆C >=0.16, then the effect is “E”

When 0.036 <= ∆C <0.16,

if ∆C > = ∆C0, then the effect is “E”

if ∆C < ∆C0, then the effect is “L”

We applied the above procedure to ATC displays to 

compute the effectiveness of color in Identification tasks 

for CDs. The background color was assumed to be at 

20% gray. The computation was made for the same ten 

colors as those in Table 1. In addition, we added three 

typical default colors and one background color to the 

list: white, gray, yellow-green, and black. The results 

are shown in Table 2. A majority of the data in Table 2 

are “L” and “NE,” suggesting that many of the colors 

are either less effective or not effective in identification 

tasks for CDs. 

Segmentation

The effects of color for Segmentation mainly depend 

on the color difference of the object and its surround. 

While the chromaticity information is processed first 

in visual segmentation, some achromatic cues such as 

luminance differences can also result in segmentation 

when no color difference is present. Since the effect of 

segmentation is largely determined by the uniformity 

Table 1. Effect of color in Attention for CDs relative to CNs. 

Distractor 100% distractor luminance 50% distractor luminance 

Target Green White Yellow-green Green White Yellow-green 

Red NE NE NE L E E 

Green NE L NE NE E E 

Yellow L L NE L E E 

Blue NE NE NE NE L L 

Purple NE NE NE L L L 

Brown NE NE NE L L L 

Orange L L L E E E 

Pink L NE L L L L 

Magenta NE L NE L L L 

Cyan L L L E E E 
of an object or a local visual region, achromatic cues are 

not as effective as color unless the object or the area is 

completely uniform. According to our literature review, 

the threshold color difference for segmentation is about 

0.012 and the threshold luminance difference is about 

20% of the baseline luminance of the object to be segmented. The effects of segmentation increase with the 

differences between an object and the surround. Based 

on these results, we computed the effectiveness of color 

for Segmentation using the following steps: 

1. Convert the images of a color display into the images 

as seen by CDs; 

2. Determine the chromaticity values of the object and 

surround colors for CDs;

3. Determine the chromaticity values of the object and 

surround colors for CNs; 

4. Compute the color difference ∆C between the object 

and surround, and the relative luminance difference 

∆RL, defined as the luminance difference between 

the object and surround divided by the object luminance; 

5. Compare the differences to critical points to determine 

the effects of color:

If ∆C < 0.012 and ∆RL < 0.2 for CNs, the effect 

is “NA”

Else,

If ∆C < 0.012 and ∆RL < 0.2 for CDs, then the 

effect is “NE”

If ∆C >= 0.012 for CDs and ∆C is equal or greater 

for CDs than for CNs, then the effect is “E”

If ∆C >= 0.012 for CDs and ∆C is less for CDs than 

for CNs, then the effect is “L”

If ∆C < 0.012 for CDs and ∆RL >=0.2 for CDs, 

the effect is “L”

We applied the above algorithm to ATC displays to 

compute the effects of color for Segmentation by CDs. 

The results are listed in Table 3, with rows for object 

color and columns for surround color. Notice that, except 

the identical object-surround colors (those along the 

diagonal line of the Table), all the other combination of 

colors are effective for segmentation by CDs and CNs. 

The comparisons between CDs and CNs are either “E” 

or “L,” but not “NE,” indicating that color is an effective 

cue in segmentation tasks even for CDs. Furthermore, 

some “less effective” color combinations for CDs do not 

necessarily mean less effective in real-life task performance. 

As long as an object, such as the menu bar of a display, 

can be segmented from its surround, observers can direct 

their eyes to that location, regardless of greater or less 

segmentation. 

Text readability

Text readability is mainly determined by luminance 

contrast between the text and background. Readability 

increases with luminance contrast. When luminance contrast is near the threshold for reading, a color difference 

can sustain reading but is not as effective as the luminance 

factor. Moreover, there is no additive effect between color 

contrast and luminance contrast in readability. Therefore, 

we only considered the contribution of the luminance 

factor to reading. Based on the literature review, we 

took 20% Michelson contrast as the threshold point for 

reading. Hence, we computed the effects of color in text 

readability for CDs using the following steps: 

1. Convert the images from a color display into the images that would be perceived by CDs; 

2. Determine the chromaticity values of text and background colors for CDs;

Table 2: Effect of color in Identification for CDs relative to CNs. 

 Red Green Yel Blu Pur Bro Oran Pink Cyan Mag Black White Gray YG 

Red NA NE NE L L NE L NE L L L L L NE 

Green NE NA NE E E NE L NE E NA E E E NE 

Yellow NE NE NA L E NE E NE L E L L L NE 

Blue L E L NA NE L L E E L E E E E 

Purple L E E NE NA L L E NA NE E E E E 

Brown NE NE NE L L NA E NE L E L L L NE 

Orange L L E L L E NA L NE L NE NE NE L 

Pink NE NE NE E E NE L NA L NA E E E NE 

Cyan L E L E NA L NE L NA E NE NE NE L 

Magenta L NA E L NE E L NA E NA NA NA NA E 
10

3. Determine the chromaticity values of text and background colors for CNs; 

4. Compute Michelson contrast for CDs: C=(Lt-Lb)/

(Lt+Lb), where Lt is the text luminance and Lb is the 

background luminance; 

5. Compute Michelson contrast C0 for CNs; 

6. Compare the contrasts to critical points for text readability to determine the effects of color for reading: 

If C0 < 0.2, then the effect is “NA”

Else

If C < 0.2, then the effect is “NE”

When C >=0.2 ,

If C >=C0, then the effect is “E”

If C<C0, then the effect is “L”

We applied the above procedure to ATC displays. We 

calculated and compared the text readability of 14 colors 

by CDs and CNs. The results are shown in Table 4, with 

the columns for background color and rows for text color. 

When determining the background luminance, notice 

that even a background in its lowest rgb setting (r=g=b=0) 

still has some residual luminance due to illumination of 

visual stimuli on a computer screen and ambient light. 

An easy way to estimate the residual luminance is by 

simply treating a dark screen as a setting of 20% gray. 

For most computer monitors, the luminance produced 

by rgb values below 20% of the maximum rgb setting 

can hardly be discriminated by human eyes. With this 

assumption, the black color in Table 4 is actually specified with r=g=b=50. Notice that many combinations of 

text – background colors result in “NA” even for CNs. 

There are also many cases of “NE” for CDs. Therefore, 

colors should be chosen carefully when used to highlight 

a text because the highlighting color may result in low 

text readability. 

Effect of redundant cues

One argument about the effects of using color in 

displays on the task performance of CDs is that they can 

use redundant achromatic cues to acquire color-coded 

information. Redundant cues in ATC displays include 

flashing, location, brightness/luminance, shape, size, and 

text. The literature review has shown that achromatic cues 

are not always as effective as colors. We next synthesize 

the results. 

Redundant cues for attention:

•	 Flashing has been demonstrated to be the most effective cue in drawing attention. Thus, its effectiveness 

is greater or at least equal to color;

•	 A stimulus location by itself cannot draw attention; 

however, a sudden onset of a stimulus can draw attention to a known location. In this case, location can be 

equally effective to colors;

•	 High luminance / brightness is equal to or more effective than colors;

•	 Shapes can draw attention only if they are significantly 

different from distractors; however, they are far less 

effective than colors;

•	 Size and text are of limited use in drawing attention.

Redundant cues for identification:

•	 Flashing and luminance are not effective in identifying 

different categories of information because the visual 

system does not reliably register the levels of flashing 

or luminance in the mental processing;

Table 3: Effect of color in Segmentation for CDs relative to CNs. 

 Red Green Yel Blu Pur Bro Oran Pink Cyan Mag Black White Gray YG 

Red NA L L L L L L L L L L L L L 

Green L NA L E E L L L E E E E E L 

Yellow L L NA L E L E L L E L L L L 

Blue L E L NA L L L E E L L L L E 

Purple L E E L NA L L E E L E E E E 

Brown L L L L L NA E L L E L L L L 

Orange L L E L L E NA L L L L L L L 

Pink L L L E E L L NA L E E E E L 

Cyan L E L E E L L L NA E L L L L 

Magenta L E E L L E L E E NA E E E E 

Black L L E L E L L E L E NA L L E 

White L L E L E L L E L E E NA E E 

Gray L L L L E L L E L E E L NA E 

Yellowgreen L L L E E L L L L E E E E NA 
11

•	 Location is effective only in situations where the types 

of information are fully correlated with locations; 

•	 Size can be used to identify information only up to 2 

or 3 categories (big, small, and maybe medium). Size 

is less effective than color or not effective at all when 

identification tasks are difficult; 

•	 Shape and text are less effective than color for tasks 

involving complex scenes. 

Redundant cues for segmentation:

•	 Color is superior to achromatic cues in segmenting 

complex visual scenes; 

•	 Flashing is not an appropriate cue for information 

organization; 

•	 Location is effective for segmentation, provided that 

different categories of information are spatially wellseparated;

•	 Luminance is a less effective cue for segmentation than 

color;

•	 Shape and size may be used for organizing information 

because differences in spatial frequency andorientation 

of visual stimuli can result in figure-background 

segmentation; these cues are much less effective than 

color.

•	 Text is not effective in segmentation because of its 

heterogeneity. 

Table 5 summarizes the above results. The three types 

of tasks, Attention, Identification, and Segmentation are 

listed in rows, and achromatic cues are listed in columns. 

The effectiveness of achromatic cues relative to colors is 

represented in three scales: “E” refers to the situation where 

the achromatic cue is of equal effectiveness to color, “L” 

means that the achromatic cue is less effective than colors, 

and “NE” means that the attribute is not effective for 

the given task. In addition, “NA” represents the situation 

where color is not effective. In this case, no comparison 

between color and achromatic attributes was made. Many 

elements in Table 5 are “L” and “NE,” suggesting that, 

in many situations, the presence of redundant cues may 

not help CDs to perform color-related tasks as much as 

they do for CNs. 

Table 4: Effect of color in text readability for CDs relative to CNs. 

 Red Green Yel Blu Pur Bro Oran Pink Cyan Mag Black White Gray YG 

Red NA E E NE NE E E NE E NA NE E E E 

Green E NA NA E E L NA NA NA E E NE NA NA 

Yellow E NA NA E E L L NA NA E E NA E L 

Blue NE E E NA NA NA E NE E L NA E E E 

Purple NE E E NA NA NA E NE E L NA E E E 

Brown E L L NA NA NA NE L L L NA L NE NE 

Orange E NA L E E NE NA NA E E E L NA NA 

Pink NE NA NA NE NE L NA NA NA E NE E NA NA 

Cyan E NA NA E E L E NA NA E E NA NA E 

Magenta NA E E L L L E E E NA L E E E 

Black NE E E NA NA NA E NE E L NA E L L 

White E NE NA E E L L E NA E E NA E L 

Gray E NA E E E NE NA NA NA E L E NA NA 

Yellowgreen E NA L E E NE NA NA E E L L NA NA 

Table 5: Effect of achromatic redundant cues relative to colors. 

 Flashing Location Luminance Shape Size Text 

Attention E L E NE NE NE 

Identification NA E NE L NE L 

Segmentation NA L L L L NE 
12

DISCUSSION AND CONCLUSIONS

This report synthesized previous visual studies about the 

effects of color on Attention, Identification, and Segmentation tasks, as well as text reading. Accumulative research 

has provided us with a profound understanding of color 

information processing. The purpose of this report was 

to apply those results to assess the potential effects of 

color vision deficiencies on ATC task performance. We 

first synthesized the data in the literature. Based on those 

results, we then developed algorithms to compute the effectiveness of color on the task performance by CDs. We 

also analyzed the effectiveness of the presence of achromatic 

redundant cues relative to color. Combining these, we are 

able to assess the overall impact on the performance by 

CDs in using color displays. 

We provided a set of look-up tables and algorithms to 

assess the impact of color vision deficiencies for performance 

of each of the primary tasks: attention, identification, 

segmentation, and text reading The look-up tables are easy 

to use, but the results are available only for a limited set 

of colors. The look-up tables are based on some general 

assumptions about ATC displays. The effectiveness of color 

for CDs was simplified into three levels: Color was equally 

effective for CDs as it was for CNs, less effective relative to 

CNs, and non-effective. On the other hand, the algorithms 

allow more quantitative comparisons between CDs and 

CNs. One shortcoming is that both the algorithms and 

the look-up tables are based on Brettel’s algorithm (1997) 

that simulates severe color vision deficiencies, in which one 

or two types of cones are completely absent from retinas. 

Many individuals have mild color vision deficiencies, in 

which the number of a certain type of cones is less than 

normal, or the cones are less sensitive than normal. 

The algorithms in this report were developed to address 

the general concerns associated with the performance of 

color vision deficient controllers on the newer color displays 

that have been introduced in ATC facilities. However, they 

can be extended to evaluate other aspects of ATC displays. 

Without converting color images into what would be seen 

by CDs, we can use the algorithms to assess the effectiveness 

of color-coding for CNs. While it is desirable that color 

be used effectively for CNs, it is not always true for ATC 

display designs. The look-up tables contain many elements 

labeled as “NA,” meaning that the specific combination 

of colors is not effective even for observers with normal 

vision. Therefore, such combinations should be avoided 

in ATC displays. For example, red-colored messages have 

been frequently used for emergency alerts. However, 

because red does not produce a high luminance, a red 

target often appears dimmer than other materials on a 

display. As the result, red-colored text is not very effective 

in drawing attention. 

Finally, we need to point out that the algorithms and 

look-up tables are based on experimental data obtained 

in research labs. There are differences between situations 

in labs and operational facilities. For instance, subjects in 

research labs are assumed to fully attend to given tasks; 

their performance may be better than in an operational 

setting, where controllers are required to simultaneously 

perform several tasks, and they may not fully focus their 

attention on a single task. On the other hand, controllers 

may use color-coded information more efficiently than 

subjects in labs because controllers have been exposed to 

the same kind of visual stimuli on a daily basis for some 

time. Their experience and familiarity with color-coding 

could improve the efficiency of color use. In addition, the 

effects predicted by the algorithms may differ, depending 

on the extent of the color vision deficiency. We also made 

no effort to separate the use of color for the more critical 

safety-related tasks against those tasks that are less critical. 

Therefore, the results provided by this report should only 

be used for an initial assessment. Once we determine the 

situations in which color-coding may not be effective for 

CDs, high fidelity experiments will follow to examine the 

exact effects of color on CDs’ task performances. 

REFERENCES

Banks WW, Weimer JJ (1992). Effective computer display 

design. Prentice-Hall: Englewood Cliffs, NJ.

Berlin B, Kay P (1969). Basic Color Terms. Berkeley and 

Los Angeles: University of California Press.

Boynton RM, MacLaury R, Uchikawa K (1989). Centroids of color categories compared by two methods. 

Color Res Appl; 14: 615.

Boynton RM, Olson CX (1990). Salience of chromatic 

basic color terms confirmed by three measures. 

Vision Res; 30(9): 1311-7. 

Brettel H, Vienot F, Mollon JD (1997). Computerized 

simulation of color appearance for dichromats. J 

Opt Soc Am A; 14(10): 2647-55. 

Carter EC, Carter RC (1981). Color and conspicuousness. J Opt Soc Am A; 71: 723–9. 

Christ RE (1975). Review and analysis of color coding 

research for visual displays. Hum Factors; 17(6): 

542-70.

Cowan WB, Ware C (1987). On the Brightness of colours 

that differ in hue or saturation, Proceedings of the 

Society For Information Display; 28(4): 312-4.
13

Johnson WW, Liao MJ, Granada S (2002). Effects of symbol brightness cueing on attention during a visual 

search of a cockpit display of traffic information. 

Proceedings of the Human Factors and Ergonomics

Society 46th Annual Meeting (pp. 1599-603); Santa 

Monica, CA: HFES.

Kahneman D, Treisman AM (1984). Changing views 

of attention and automaticity. In Parasuraman R, 

Davies DR (Eds.): Varieties of attention (pp. 29-61). 

New York: Academic Press.

Knoblauch K, Arditi A, Szlyk J (1991). Effects of chromatic and luminance contrast on reading. J Opt 

Soc Am A; 8(2): 428-39. 

Legge GE, Parish DH, Luebker A, Wurm LH (1990). 

Psychophysics of reading. XI. Comparing color 

contrast and luminance contrast. J Opt Soc Am A; 

7(10): 2002-10. 

Luder C, Barber P (1984). Redundant color coding 

on airborne CRT displays. Hum Factors; 26(1): 

19-32.

McIlhagga W, Hine T, Cole GR, Snyder AW (1990). 

Texture segregation with luminance and chromatic 

contrast. Vision Res; 30(3): 489-95. 

Nagy AL, Sanchez RR (1990). Critical color differences 

determined with a visual search task. J Opt Soc Am 

A; 7(7): 1209-17. 

Nagy AL, Sanchez RR (1992). Chromaticity and luminance as coding dimensions in visual search. Hum 

Factors; 34(5): 601-14. 

Nagy AL, Sanchez RR, Hughes TC (1990). Visual search 

for color differences with foveal and peripheral vision. J Opt Soc Am A; 7(10): 1995-2001.

Nothdurft HC (1993). The role of features in preattentive vision: comparison of orientation, motion and 

color cues. Vision Res; 33(14): 1937-58. 

Ojanpaa H, Näsänen R (2003). Effects of luminance 

and colour contrast on the search of information 

on display devices. Displays; 24, 167-78.

Pastor S (1990). Legibility and subjective preference for 

color combinations in text. Hum Factors; 32(2): 

157-71.

Pinker S (1984). Visual cognition: An introduction. 

Cognition; 18: 1-63. 

Poirson AB, Wandell BA (1993). Appearance of colored 

patterns: Pattern-color separability. J Opt Soc Am 

A; 10(12): 2458-70. 

Sachtler WL, Zaidi Q (1992). Chromatic and luminance 

signals in visual memory. J Opt Soc Am A; 9(6): 

877-94. 

Scharff LF, Ahumada AJ Jr (2002). Predicting the readability of transparent text. J Vis; 2(9): 653-66. 

Smallman HS, Boynton RM (1990). Segregation of basic 

colors in an information display. J Opt Soc Am A; 

7(10): 1985-94. 

Travis DS, Bowles S, Seton J, Peppe R (1990). Reading 

from color displays: A Psychophysical Model. Hum 

Factors; 32(2): 147-56.

Treisman A, Souther J (1985). Search asymmetry: A 

diagnostic for preattentive processing of separable 

features. J Exp Psychol Gen; 114(3): 285-310. 

Treisman AM, Gelade G (1980). A feature-integration theory of attention. Cognit Psychol; 12(1): 

97-136. 

Van Nes FL (1986). Space, colour and typography on 

visual display terminals. Behav and Information 

Technol; 5(2): 99-118.

Vienot F, Brettel H, Mollon JD (1999). Digital video 

colour maps for checking the legibility of displays 

by dichromats. Color Res Appl; 24(4): 243-51.

Wandell BA (1995) Foundation of vision. Sinauer Press: 

Sunderland, MA.

Wyszecki G, Fielder GH (1971a). New color-matching 

ellipses. J Opt Soc Am A; 61(9): 1135-52. 

Wyszecki G, Fielder GH (1971b). Color-difference 

matches. J Opt Soc Am A; 61(11): 1501-13. 

Xing J, Schroeder DJ (2006). Reexamination of color vision standards. I. Status of color use in ATC displays 

and demography of color-deficit controllers. Washington, DC: FAA Office of Aerospace Medicine; 

FAA technical report no. DOT/FAA/AM-06/2.

Yamagishi N, Melara RD (2001). Informational primacy 

of visual dimensions: Specialized roles for luminance 

and chromaticity in figure-ground perception. 

Percept Psychophys; 63(5): 824-46. 

Yantis S, Jonides J (1996). Attentional capture by abrupt 

onsets: New perceptual objects or visual masking? J Exp Psychol Hum Percept Perform; 22(6): 

1505-13. 

Young TL, Nagy Al (2003). Combining information 

about color and line length in visual search. J Vis; 

3(9): 704

A

APPENDIX A

A Transformation Between Color Systems 

Colors in a computer display can be described by the tri-stimulus color system. This system specifies 

a color with three photometric quantities: R, G, and B. Given (r, g, b) as the 8-bit DAC values for each of 

the red, green, and blue channels of a monitor, the relationship between RGB values and DAC values is 

determined by the following equations: 

R=(r/255)2.2

G=(g/255)2.2 

B=(b/255)2.2 

The transformation between the RGB system and CIE chromaticity (Lxy) is determined by the 

following equations: 

X=40.9568*R + 35.5041*G + 17.9167*B; 

Y=21.3389*R + 70.6743*G + 7.98680*B; 

Z=1.86297*R + 11.4620*G + 91.2367*B; 

And,

x=X/(X+Y+Z) 

y=Y/(X+Y+Z) 

L=Y

Note that the parameters in these transformations vary from monitor to monitor. The parameter values 

used here are typical default values for CRT displays. 

The CIE 1976 uniform chromaticity coordinates u ́, v ́ are given by 

 u ́ = 4x / (-2x+12y+3) 

 v ́ = 9y / (-2x+12y+3)