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Traffic signal color recognition is a problem for both protan and deutan color-vision deficients.


Approximately 8% of males and 0.5% of females in the population have congenital red-green color-deficient vision. There is little true color blindness (~0.0005%; Pokorny, Smith, Verriest, & Pinckers, 1979). These people have reduced ability to discriminate redness-greenness throughout the full gamut of colors. Most significantly, from a safety point of view, the problem includes the red, orange, yellow, and yellow-green parts of the visible spectrum.

Red-green color-vision deficiencies are subdivided into a number of categories (after von Kries, 1899):

People with dichromasy lack one of the three normal receptors. As a consequence, their ability to discriminate colors is two- rather than three-dimensional. The two forms are protanopia and deuteranopia, lacking the long-wavelength ("red") receptor or the middle-wavelength ("green") receptor; respectively. Their ability to discriminate a red, yellow, and yellow green signal code on the basis of color is absent, and they must rely on the usual brightness hierarchy that yellow is brighter than yellow green, which is brighter than red. The use of a bluish green rather than yellowish green signal solves this problem for them. In addition, in protanopia, red signals are seen as substantially darker and are less alerting. A red traffic signal as seen in protanopia has approximately 25% the luminous intensity as it has for a color normal (Dain & King-Smith, 1981). Protanopia and deuteranopia each constitute about 1% of males.

People with anomalous trichromasy have one receptor altered as compared with a color normal. As a consequence, their ability to discriminate colors is reduced rather than absent in the third dimension. The two forms are participants and 49 color-vision deficient participants, divided into 25 deutans and 24 protans selected according to the criteria shown in Table 1. All participants had binocular visual acuity of 6/6 or better, but to achieve this 11 participants wore (untinted) ophthalmic corrections.

The experimental setup is shown in Figure 1. The participant viewed a fixation target placed in the center of a computer monitor at a distance of 4 m. Simulated single-aspect traffic signals were briefly displayed at small angles to either side of the direction of fixation. The participant's task was to identify the color as quickly as possible. The single light presentation was adopted because the positional clues in a laboratory-based three-aspect lantern would be consistent and significant. In the real driving task the positional clue is not always available.


The two lights were located 5[degrees] away from the participant's line of sight (10[degrees] apart). The angular subtense of the lights was equivalent to that of a 200-mm traffic signal lantern at a distance of 100 m (2 mrad or 6.9 arcmin). This is the standard Australian practice as detailed by Fisher and Cole (1974) and AS/NZS2144:2002 (Standards Australia International, 2002). The same specifications appear in CIE publications (CIE, 1977, 1980). The lights were created using 20-W, 12-V tungsten halogen globes with appropriate filters to provide the appropriate traffic signal chromaticity coordinates specified in AS/NZS 2144:2002, and the intensity was controlled using neutral density filters. The chromaticity coordinates shown in Figure 2 were calculated according to CIE (1986). The lights were turned on for a maximum duration of 5 s. Rise time to full intensity was approximately 200 ms.


The backgrounds to these lights were black backboards scaled in accord with the backgrounds around normal traffic lights (Fisher & Cole, 1974; Standards Australia International, 2002). Between the two lights was a computer monitor. Around the monitor and the black backboards was a white matte board. Two fluorescent light tubes illuminated this so that it provided a luminance of 300 cd/[m.sup.2] for the participant. The participant was shielded from a view of the light tubes. Traffic signals may operate over a range of intensity levels. The minimum and maximum for both red and green are 200 cd and 1000 cd, respectively, and the minimum for yellow is 600 cd. We presented lights of low and high intensity. The low-intensity lights were 0.32 cd for red and green and 0.96 cd for yellow, which are the 4-m equivalent of the 200-mm traffic signal at 100 m, complying with the minimum AS/NZS2144:2002 requirements. The high-intensity lights were twice the minimum requirement: 0.64 cd for red and green and 1.92 cd for yellow.

The experiment was divided into three sections: button reaction time, practice, and the experiment proper.

The first section involved measuring reaction times for each finger on a three-button mouse that was adapted to measure responses in the experiment. This was necessary in order to take into account the different dexterities among participants and among fingers of any participant. If a participant was right handed, his index finger was on the left mouse button, his middle linger on the middle button, and his ring finger on the right button. If a participant was left handed, this sequence was reversed. Each participant was instructed to rest his hand comfortably on the mouse with the fingers in the correct position. To illustrate this, a drawing of a mouse with three buttons was presented on the computer screen and, randomly, one of the drawn buttons was highlighted. The participant pressed the button to which it corresponded. The participant was reminded of the need for both speed and accuracy. Two runs of 5 presentations of each button were made (i.e., 10 presentations for each button). Reaction times were averaged after discarding mistakes. For data analysis, response times for a button were adjusted by subtracting the reaction times for that button.

The second section consisted of six practice runs of the experiment. Subsequent analysis showed that all participants achieved their peak response by the fourth run.

In the experiment we simulated driving using a divided-attention task (Moskowitz, 1974). The fixation target was a 1.5-cm diameter circle on the computer monitor. The participant was asked to place this inside a 1.5 x 2 cm rectangle, which moved in straight lines at random speeds and directions, by moving the computer mouse held in the preferred hand. For the periods in which the participant succeeded in this task, he received feedback by the circle changing into a cross. At random intervals of between 6 and 12 s, one of the lights, either to the left or the right, was turned on. The participant was required to abandon the tracking task, identify the color as quickly as possible, and indicate this by pressing one of three buttons of the computer mouse: left button for red, middle button for yellow, and right button for green. This coding was the same regardless of which hand was used. The participant was given no feedback about which lights were correctly or incorrectly identified. Failure to respond within 3 s was regarded as a failure of detection. After the response, or upon failure to respond within 5 s, the next sequence started.

The participant was instructed to immediately inform the experimenter if he made a mistake in responding to a light. For example, if he knew a light was green but had pressed the "red" button accidentally, he told the experimenter, and this was noted in the appropriate location in the sequence on a manual recording sheet. This was later correlated with the computer's record of responses, and these "mistakes" were not used in any analysis (the mean mistake rate per sunglass was 1 to 4 across the participants).

Presentation of targets and recording of responses was under computer control. A run consisted of 12 presentations, with 1 presentation on each side of low- and high-luminance red, yellow, and green lights. These presentations were randomized within each run. There were four runs, so each color was presented 16 times.

A short break was given between runs. The experiment reported here, of traffic signal recognition with an untinted lens, was randomized within a set of experiments with the same procedure but using tinted lenses. The experiments using the tinted lenses will be reported elsewhere. Approximately 2 hr of experimental time were required for each participant, including rest breaks.

Analysis of Results

Separate analyses were done for mean adjusted response times and for response accuracy. The participants were divided into five groups: normals, deuteranomals, deuteranopes, protanomals, and protanopes. For the response times, assumption of normality was assessed by the Shapiro-Wilk test for each of the nine participant subgroups (four additional subgroups were created by placing anomalous trichromats in severity categories) and found to hold for all signals. Parametric analyses of variance (ANOVAs) were then used. If the signal color was called incorrectly, its time was still included in the response time analysis, as we found that the mean times for incorrect and correct responses were not significantly different when using paired sample t tests. For the error analysis, assumption of normality was again tested. Although some participant group-signal color combinations having few errors gave nonnormal distributions, we proceeded with parametric ANOVAs. For all ANOVAs giving significant results, post hoc pair-wise comparisons were made with the Bonferroni adjustment for multiple comparisons. A 5% level of significance was used for all tests.


Response Times

Figure 3 shows the mean adjusted response times for each of the five participant groups for the red (R), yellow (Y), and green (G) signals. Figure 4 is a more detailed analysis in which the deuteranomals and protanomals are divided into mild, moderate, and severe subgroups.


A mixed ANOVA with one within-subjects factor (signal color) and one between-subjects factor (color deficiency) demonstrated that the main effects of signal color, F(2, 128) = 64.21, p < .001, and color-deficiency group, F(4, 64) = 9.45, p < .001, were significant, and there was also a significant interaction between signal color and group, F(8, 128) = 5.36, p < .001.

Accordingly, we performed a series of one-way ANOVAs for each signal color. There were significant between-group variations for R and Y signals, F(4, 64) =9.816, p< .001, and F(4, 64) = 11.561, p < .001, respectively, but not for G signals, F(4, 64) = 1.898, p = .122. Post hoc analysis for R signals shows that normals have significantly shorter response times than do all color-deficient groups except for protanomals, and that protanomals have significantly shorter reaction times than do deuteranopes. A similar pattern occurs with Y signals, except that both deuteranomals and protanomals have significantly shorter response times than do deuteranopes.

No statistical analysis was done using the deuteranomalous and protanomalous subgroups because of the small number (5) in each subgroup, but a clear trend of increasing response time with increasing severity is apparent for the deuteranomalous participants for both R and Y signals (Figure 4).

Repeated group ANOVAs for each participant group were used to compare the response times to the different signal colors. For each participant group, there were significant differences among the response times (p [less than or equal to] .002). For each group except tot the normal group, shorter response times occurred for G signals than for Y signals. For each group, shorter response times occurred for Y signals than for R signals. The differences are statistically significant, except for normal participants between G and Y signals, for deuteranomals between G and Y signals, for deuteranopes between R and Y signals, for protanomals for Y signals with both R and G signals, and for protanopes for Y signals with both R and G signals.

The main features of Figures 3 and 4 indicate the following:

1. The response times of color deficients increased compared with those for the color normals for both the R and Y signals.

2. Deutans performed worse than protans (of the same severity) for R and Y signals. The mild and moderate protanomals performed a little worse than the normals. The deuteranopes showed the greatest increases in response time, 53% for R and 85% for Y, relative to those of normals. However, protanopes showed increases in response times of 35% for R and 53% for Y. relative to those of normals.

3. Response times to G signals were not affected significantly by category of defect.

The purpose of using the two intensity levels for each signal color was to represent the variation of intensities of traffic signals and, in the context of the experiment, to minimize participants' learning to use intensity as a cue to color. However, we did some analysis of how luminance affected response times. Overall, the color defectives were quicker for the dimmer than for the brighter R signal. All subgroups (including normals) were quicker for the brighter than for the dimmer Y signal, with the difference increasing with severity and being greater for deutans than for protans of the same severity (except dichromats). There were no notable differences between the response times for bright and dim G signals for any of the subgroups.


Figure 5 shows mean percentage errors for each of the five participant groups for the R, Y, and G signals. Figure 6 shows a more detailed analysis, in which both the deuteranomals and protanomals are divided into mild, moderate, and severe subgroups.


Again, a mixed ANOVA with one within-subject factor (signal color) and one between-subjects factor (color deficiency) demonstrated that the main effects of signal color, F(2, 128) = 17.38, p < .001, and color-deficiency group, F(4, 64) = 40.54, p < .001, were significant, and there was also a significant interaction between signal color and group, F(8, 128) = 5.39, p < .001.

Accordingly, we performed one-way ANOVAs for each signal color. There are significant between-subject group variations for R, F(4, 64) = 15.876, p < .001, and Y signals, F(4, 64) = 9.343, p < .001. No errors occur for the G signal for any participants.

The errors for R and Y signals show a trend similar to that for response times in Figures 3 and 4. Generally, errors increased as the severity of the color vision deficiency became more marked. Except for the mild subcategory, deutans, overall, performed worse than protans of the same severity category (Figure 6). Protanomals actually performed about as well as normals. Deuteranopes made significantly more errors than did any other group (p .001), with 30% and 23% errors for R and Y signals, respectively.

For all participant groups, the main errors were in calling a R signal as Y and vice versa. This is demonstrated for deuteranopes in Figure 7.


Across all participant subgroups, most R signal errors were made with the brighter signal and most Y signal errors were made with the dimmer signal. This is consistent with differences in response time to the two luminance levels of these signals.


This investigation agrees with earlier studies in that color deficients have longer response times and make more mistakes than do color normals when responding to signals. However, contrary to previous investigations (Nathan et al., 1964; Pun et al., 1986; Cole & Vingrys, 1983), we have found that deutans perform worse than protans. Furthermore, our increases in response times and error rates of color deficients are less than those found in the Nathan et al. study. For example, our deuteranopes showed the greatest increases in response time--53% for R signals and 85% for Y signals, relative to those of normals--whereas protanopes showed increases in response times of 35% for R signals and 53% for Y signals. In the Nathan et al. study, the respective response time increases were 88%, 95%, 105%, and 118%. Another difference between this and previous work is that we found no effect of color-vision deficiency on response times to G signals, whereas Nathan et al. found that deuteranopes and protanopes had increased response times of 48% and 86%, respectively.

It is possible to partially reconcile the differences in our results with those of previous studies. We used single R, Y, and G colors, rather than the several variations used in the Nathan et al. (1964) study, which made the task simpler for the color deficients in our study. Our findings are more in line with those of Cole and Brown (1966), who found that the response time of protanopes to an R signal was never more than 50% greater than that for normals at any luminance--as they used only an R signal, there was no time lost in making decisions about the correct color. No errors were made to the G signal in our study, which indicates that this green chromaticity gamut is well chosen. Judging by the low missed-signals rate (e.g., Figure 7), our signals are well above threshold for all color deficients, and under these circumstances protans are no longer at a disadvantage relative to deutans.

Protans perfoma worse when lights are closer to threshold, such as in experiments measuring the closest distance at which a signal can be recognized (Verriest et al., 1980; Cole & Vingrys, 1985). With highly visible signals, protans may be able to use intensity cues to distinguish between colors. Having two levels may have reduced intensity cues in our study, but the factor of two between them may not have been enough to make much difference. Also, some differences could be attributable to modern familiarity with a blue green rather than yellow green signal color. In addition, any study that looks at detection rather than recognition as the participant's task will be more likely to demonstrate the loss of red sensitivity of the protan.

The difficulties in distinguishing red and yellow occurred despite the yellow signal having three times the intensity of the red signal. Given that the higher intensities required in yellow signals are less easily attainable in signals composed of LEDs, there may be pressures to reduce the required intensities, but the present study should be a discouragement to that move. It should also be noted that modern practices using red LEDs will exacerbate the problems of detection of red signals, because the LED signals are redder than the conventional signal.

Given the multifactorial nature of road crash causation and the failure to collect the necessary driver data, it is not surprising that few data linking color-vision deficiencies are available. Despite this, a link with protan color-vision deficiencies has yet to be eliminated as an issue (Cole, 2002a, 2002b; Vingrys, 2002b; Wolfe, 2002). At present, the only color-vision requirements in driving usually relate to protans (given their demonstrated problem in detecting red signals) in high-exposure jobs where the consequence of error may be greater (heavy goods vehicles, taxis, and buses). The results of the study reported here indicate that deutans--in particular, deuteranopes--also merit close scrutiny in driving performance.
TABLE 1: Color Vision Deficient Groups

Extent               Selection Criteria               Deutan   Protan

Mild anomalous       Pass Farnsworth lantern, pass      5        5
trichromats          Farnsworth-Munsell Panel D-15

Moderate anomalous   Fail Farnsworth lantern, pass      5        5
trichromats          Farnsworth-Munsell Panel D-15

Strong anomalous     Fail Farnsworth-Munsell Panel      5        5
trichromats          D-15 (but not dichromats or
                     extreme anomalous
                     trichromats (a))

Dichromats           Match whole red-green range on     10       9
                     Nagel anomaloscope even after
                     adaptation on Trendelenberg

Note. The Farnsworth lantern contains nine pairs of colored lights.
Colors involved are green, red, and white. A pass is 2 or fewer
identification errors on two runs. The Farnsworth-Munsell Panel
D-15 test involves arranging 15 caps in order of color; color
deficients of sufficient severity make particular types of
arrangement errors. The Nagel anomaloscope requires participants
to match various red-green light mixtures with a yellow light.

(a) Extreme anomalous trichromats are defined on three criteria.
They accept matches at one extreme of the Nagel anomaloscope
range, they accept the normal match, and they demonstrate high
variability in the apparent extent of their deficiency. This has
been described as "tuning" of their range after neutral adaptation
(the Trendelenberg plate on most anomaloscopes; Pokorny et al., 1979).
They were excluded from this study.


A Queensland University of Technology Research Encouragement Award supported this study. Eddie Matejowsky wrote the computer program and built the electronic circuitry for the experiment. Nancy Spencer provided statistical advice.


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David A. Atchison received his Ph.D. in optometry in 1984 from the University of Melbourne. He is an associate professor in the School of Optometry, Queensland University of Technology.

Carol A. Pedersen received her BAppSc(Optom) (Hons) in optometry in 1992 from the Queensland University of Technology. She is an optometrist and senior research assistant in the School of Optometry, Queensland University of Technology.

Stephen J. Dain received his Ph.D. in optometry in 1972 from City University (London). He is head of the School of Optometry and Vision Science, University of New South Wales.

Joanne M. Wood received her Ph.D. in optometry in 1987 from the University of Aston. She is an associate professor in the School of Optometry, Queensland University of Technology.

Address correspondence to David A. Atchison, School of Optometry, Queensland University of Technology, Victoria Park Rd., Kelvin Grove Q 4059 Australia;

Date received: October 17, 2001

Date accepted: June 2, 2003
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Author:Atchison, David A.; Pedersen, Carol A.; Dain, Stephen J.; Wood, Joanne M.
Publication:Human Factors
Date:Sep 22, 2003
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