Color recognition and discrimination under full-moon light

Color recognition anddiscrimination under full-moon light

George Smith, Algis J. Vingrys, Jennifer D. Maddocks, and Christopher P. Hely

The ability to recognize and discriminate colors under full-moon light was measured Color naming wasperformed at three sizes (0.50, 20, and 40) by the use of one white and six colored chips that spanned thespectrum at two levels of saturation. The results show that correct color recognition is possible underfull-moon light. However, the recognition rate depends on a complex interaction between hue, level ofsaturation, and size of test field. For small fields and desaturated colors, the recognition rate islow. However, for saturated colors, most hues can be recognized at better than chance levels, with redbeing recognized very accurately.

Key words: Color vision, moonlight, mesopic, color discrimination.

Introduction

In a recent court case in Australia, there was somedispute between vision experts regarding the capacityof a witness to recognize the color of a red car seen ona clear night under the light of a full moon. Aliterature search failed to find any definitive studythat considered whether a surface color could berecognized under any level of moonlight. From ourinterpretation of the literature on the perception ofcolor and from published values of moonlight levels,we would expect that it would be difficult, if notimpossible, to distinguish color even under the lightof the full moon. We set out our reasons below.

It is usually considered that night vision is medi-ated by rods that do not permit the perception ofcolor.1 Only cones provide color vision, and theseoperate at higher levels of ambient illumination.The accepted limits of cone (photopic light levels) androd (scotopic light levels) vision are given in Table 1from the CIE2 (Commission Internationale de l'Eclai-rage) and Adler.3 Between the lower limit of phot-opic vision and the upper limit of scotopic vision liesmesopic vision, which is the region in which bothcones and rods contribute to vision. This is a diffi-cult region to model because the relative amounts ofcone or rod input vary with actual light levels.

G. Smith and A. Vingrys are with the Department of Optometry,University of Melbourne, Parkville, Victoria 3052, Australia. J.Maddocks and C. Hely are optometrists in private practice.

Received 30 August 1993; revised manuscript received 1 Decem-ber 1993.

0003-6935/94/214741-08$06.00/0.C 1994 Optical Society of America.

Terrestrial illuminances under full-moon light aregiven in Table 2 from Levi.4 Table 2 shows data for arange of lunar elevations along with calculated esti-mates of ambient luminance for a perfect gray dif-fuser with a reflectance of 50%. A comparison of thedata in Tables 1 and 2 shows that the luminance ofmoonlight fails to reach those levels needed forphotopic vision, even at maximum elevation. There-fore, according to this argument, one could expectthat recognition of surface colors under moonlight isrod dominated with color discrimination unlikely.However, the amount of full-moon light given inTable 2 borders on mesopic levels (Table 1), whichsuggests that some cone input is plausible. If thiswere so, then color vision might be possible, and colordiscrimination might exist. Further evidence to sug-gest that color vision might act at these low lightlevels is given by the following observations.

Vision is considered to be mediated by threepostreceptoral processes, two color sensitive (red-green and blue-yellow) and one achromatic (lumi-nance). King-Smith and Carden6 have shown that,at threshold, vision is devoid of color sensation when-ever the achromatic process has the greatest sensitiv-ity. If intensity were then to be increased abovethreshold, the color of the object would becomeapparent when the chromatic mechanism begins tooperate. The region between the onset of the achro-matic and the chromatic processes has been termedthe photochromatic interval Figure 1, from Gra-ham and Hsia,7 shows the average relationship be-tween the achromatic and the chromatic absolutethresholds of spectral sources for six subjects withnormal color vision. The chromatic curve repre-

20 July 1994 / Vol. 33, No. 21 / APPLIED OPTICS 4741

Table 1. Photopic and Scotopic Ranges from the CIE2 and Adleraa

Luminance (cd/n 2 )

Light Level Upper Limit Lower Limit

Photopic (cones) - 3.0, 1.0bMesopic 3.0, 1.0 0.03, 0.001Scotopic 0.03, 0.001 -

aThe mesopic range is in between these two ranges, and thelimiting light values are taken as the photopic and the scotopiclimiting values.

bHere and below, the first value is that give by the CIE,2 and thesecond value is that given by Adler.3

sents the operating domain of both color (red-greenand blue-yellow) mechanisms. From this diagram itis evident that the achromatic process dominates inthe yellow-green region of the spectrum. From thesedata it could be expected that green and yellow colorswould appear colorless under moonlight. However,for longer wavelengths (> 630 nm), Fig. 1 shows thatthere is little or no photochromatic interval. Simi-larly, the two curves are very close in the blue region( <480 nm). Thus these data suggest that spectralred and blue lights might be correctly recognized attheir detection thresholds, a fact that Graham andHsia confirmed by experimental observation.

King-Smith and Carden5 have extended our knowl-edge on this subject by defining the region where theachromatic process dominates. They confirm thatachromatic thresholds are more sensitive than chro-matic thresholds at low light levels. Even at rela-tively high levels of retinal illumination (1000 Td,which correspond to a scene luminance of approxi-mately 140 cd/M2) they show that the achromaticprocess has greater sensitivity in the yellow region ofthe spectrum or for small and brief stimuli.5

Nevertheless, the issue regarding the recognition ofsurface colors at low light levels is not easily predictedby these experiments with spectral light sources.Recognition of surface colors at low light levels iscomplicated by the interaction of the reflectanceproperties of the surface with those of the ambient

Table 2. lluminance Levels on a Horizontal Surface That Are Due to theMoon,4 and Luminances of a Horizontal Perfectly Diffuse Reflecting

Surface with a Reflectance of 50%a

Horizontal Plane

Moon Illuminance Luminance Vertical PlaneElevation (°) (lux) (cd/M2 ) Illuminance (lux)

30 0.101 0.016 0.17535 0.122b 0.17340 0.143 0.023 0.17050 0.183 0.029 0.15460 0.219 0.035 0.12670 0.243 0.03980 0.258 0.04190 0.267 0.042

aThe illuminances in the right-hand column are calculated fromthe horizontal values.

bInterpolated value.

500 600Wavelength (nm)

Fig. 1. Achromatic and chromatic thresholds for foveal vision,modified after Graham and Hsia.7

illumination. In contrast to self-illumination sources,surface colors cannot have a spectral reflectanceprofile isolated to a single wavelength. When viewedunder a broadband light source, the narrower thespectral reflecting band, the less light the surfacereflects and the darker it will be. For example, asurface with a reflectance bandwidth of only 1 nmwould have a maximum luminous reflectance of-0.25% at 555 nm. Such a surface would appear

blacker than the blackest colored surface. As col-ored surfaces are not black, they must have broad-band spectral characteristics and, to some degree, bedesaturated.

The broad spectrum of a surface color would giverise to a complex interaction between the chromaticand the achromatic channels of Fig. 1 with uncertainoutcomes. Theory would predict that red and bluerecognition could occur at low light levels, as thespectral characteristics of these colors are confined tolonger and shorter wavelengths, respectively, and thechromatic mechanisms dominate in these regions.Moreover, reds would provide a minimal stimulus forthe rods, yet give a reasonable stimulus for thelong-wavelength-sensitive cones. This could resultin a color percept for a red surface color. However,the existence of this capacity has not been proven forany surface color. Perhaps the amount of lightbeing reflected is too small to stimulate the conereceptors adequately, and achromatic vision results.The nature of color discrimination for other hues isless certain or predictable.

Other attributes of surface colors, such as satura-tion, may also be expected to influence recognition.We might expect that it would be easiest to differenti-ate between two surface colors the more saturatedthey are, because then the chromatic channels wouldbe maximally stimulated. For a given hue, the higherthe reflectance, the more desaturated and lighter itmust be, whereas the more saturated a surface color,the lower its reflectance and hence the darker it willbe. The reduced light reflected from the more satu-rated (darker) color may be less than that needed toexcite a cone photoreceptor and may consequentlyproduce an achromatic sensation. Lighter colors aredesaturated by the addition of white to a hue. Thisdesaturation may serve to drown out the small chro-matic component, limiting color identification. Thus,

4742 APPLIED OPTICS / Vol. 33, No. 21 / 20 July 1994

if color vision were to act under moonlight, we couldexpect some interaction between color discriminationand saturation.

In summary, there was insufficient knowledge todecide whether the color of surfaces could be detectedand differentiated in full-moon light. Therefore wedecided to conduct an experiment to settle the issueand to take the opportunity to determine whether thesize of the target or the level of saturation affected theoutcome.

SubjectsThirteen subjects (four females and nine males),ranging in age from 20 to 51 yr, volunteered to takepart in this study. None of the subjects reported anyknown ocular disease or vision deficiency. No onewas taking medication. All reported a correctedacuity of at least 6/6 and passed the Ishihara platesviewed under daylight or with a Macbeth lamp (illumi-nant C, 200 lux).

Test Conditions: Moon Elevation, Light Level,and Weather

All measurements were made on the night closest tothe full moon for each of several months (March-June 1993), weather permitting. Measurementswere started some 3 h after moonrise and continuedfor between 90 and 120 min (3 or 4 subjects pernight). The sky was clear during most readings,although on two nights some broken clouds (2/8 to3/8) were present.

We estimated the elevation and the bearing of theMoon over the test period by using Moon movementequations from Levi.8 3 h after moonrise, the Moon'selevation was approximately 35°. Over the next 2 hthe elevation increased by 23°, and the bearingchanged by 24°. These values are approximate, asthe actual values depend on the day of the year,which, of course, varied from month to month.

The change in lunar elevation alters the illumina-tion level at the Earth, and we can refer to Table 2 tosee how this varies. The horizontal illuminancevalues indicate that the light level increased by 56%over 2 h because of increased lunar elevation (35 to600). However, although this may alter the adaptingillumination, it does not indicate the illuminance ofthe samples as they were held in a vertical planefacing the Moon. The relevant vertical illuminanceswere estimated from the horizontal illuminances bythe use of simple trigonometry and are listed in theright-hand column of Table 2. They show that theilluminance of the samples varied by only 31% overthe duration of each nightly session. Below we showthat retesting three subjects after their initial testperiod (30 min later) gave no change in their perfor-mances, which suggests that the altered ambientlight levels are unlikely to have affected the outcome.

Unfortunately we were unable to measure theactual ambient light level during the test period.There are several reasons for this limitation. First,the ambient light levels are too low for reliable

readings from most photometers. The photometersat our disposal had a minimum capacity of 1 lux,which is well in excess of the expected level (Table 2).Furthermore, accurate photometry requires that thephotometer be linear and calibrated for the particularlight source. As we did not have access to a photom-eter that was accurate at such low levels and nonewas calibrated for moonlight, we had to make use ofpublished moonlight levels.4

The experiments were conducted in a suburbanback garden. In such situations, the moonlight maybe contaminated directly by street or building light-ing or indirectly by city lighting reflected back toearth from clouds. We believe that the effects ofsuch contamination were minimal. There were noartificial lights directly visible from the test site.Furthermore, although on some nights there was abroken cloud cover, the presence of well-definedshadows led us to believe that the amount of artificiallight reflected back from the clouds was not significant.

Choice of Color Samples

Thirteen semimatt color chips were gathered fromvarious paint manufacturers: six hues (blue, green,yellow, orange, red, and purple) at two levels ofsaturation and one white. These colors were chosenby the investigators as the best-available representa-tions of these cardinal colors, and all were consideredto be easy to differentiate under daylight. Their CIE1931 (x, y) chromaticity coordinates and luminousreflectances for a CIE D65 illuminant were measuredby a Cary 2300 spectrophotometer. These valuesare listed in Table 3, and the chromaticity coordinatesare plotted on a 1931 CIE (x, y) chromaticity diagramin Fig. 2. The chromaticity coordinates and reflec-tances would be different under moonlight, but as wewere unable to obtain any spectral data on moonlight,we were unable to calculate what the coordinatesmight be under these conditions.

Table 3. 1931 CIE (x, y) Chromaticity Coordinates and LuminousReflectances of the 13 Color Chips used for the Color Recognition

Experiment, based on the D65 Illuminant

ChromaticityCoordinates

Reflectance YColor x y (%)

White 0.313 0.333 83.7Desaturated

Orange 0.362 0.367 79.7Yellow 0.404 0.428 79.1Blue 0.284 0.306 71.6Red 0.357 0.326 65.6Green 0.346 0.394 58.6Purple 0.303 0.296 57.1

SaturatedYellow 0.482 0.463 57.5Orange 0.505 0.419 38.3Green 0.327 0.465 17.3Red 0.532 0.332 15.7Blue 0.208 0.197 12.2Purple 0.308 0.247 9.6


y

0.1 -

If=\.............',................ ...............

. ~ ~ ~ ~ ~~~ ...... .............

.. . .. . . ..... .....

.~~~~~~~~~~.. .. ....

~~~~~~~~~~~~~~~~7........ . .

0.1 0.2 0.3 0.4

X

0.5 0.6 0.7

Fig. 2. Colors used in this experiment plotted on the CIE(x, y) chromaticity diagram. The coordinates and reflectanclisted in Table 3.

It can be seen from Table 3 that within thesaturated and the desaturated sets, the reflectancesof each chip are not the same. This may lead toluminance cues that assist discrimination, which is apossibility that is discussed below. However, for theexperiment we wanted to have colors of similar levelsof saturation. As a consequence, equating reflec-tance was not possible for the following two reasons.

(1) It is not possible to have a set of surface colors ofthe same reflectance and level of saturation. Thiscan be observed by reference to the Munsell colorsolid,9 which shows that the further a maximallysaturated hue is from yellow, the lower its reflectance.Therefore, if surface colors are to have the same levelof saturation, their reflectances will vary. They willbe lowest at the blue and the red ends of the spectrumand highest near the yellow to green area. To obtainsurface colors with the same level of reflectance, thereds and the blues will need to be more desaturatedthan colors in the middle of the spectrum.

(2) The luminous reflectance of an object or surfacecolor is a function of (a) its spectral reflectance R(X),(b) the spectrum of the illuminating source S(X), and(c) the spectral luminous efficiency of the observerV(X) or V'(X), and for photopic light levels the lumi-nous reflectance is defined by the equation

I R(X)S(X)V(X)dX

reflectance =

S S(X)V(X)dX

For scotopic light levels, the function V'(X) replacesV(A) in the above equation.

We measured the spectral reflectance R(X) but wereunable to obtain any information about the lunarspectrum S(X). As the light levels were in the meso-pic range, we could not use either the scotopic V'(X) orphotopic V(X) luminous efficiency function with anyconfidence. Hence luminous reflectance under moon-light could not be calculated, and we were not able toselect a set of colors that would have equal brightnessunder moonlight.

The fact that the color chips do not have the samereflectance could lead to some discrimination basedon luminance differences. We attempted to mini-mize this possibility by informing the subjects thatsome of the chips would be neutral. Subjects wereasked to use a color name only if they saw a chromaticcomponent and to use white or black if they could notdetect a color. Furthermore, the desaturated andthe saturated color sets were presented in a randomlymixed manner, so any reflectance cues would have

0.8 been difficult to learn given that subjects had not seenthe colors before, no feedback was given to the

,1931 subject, and that reflectances varied over such a largees are range (Table 3).

Procedure

Subjects were adapted to the moonlight conditions forat least 5 min before commencing the experiment.The colored chips were presented vertically so thatthey faced the Moon, behind midgray circular masks.The masks had apertures subtending 0.50, 20, and 4°at the viewing distance of 40 cm.

Each of the 13 colors (six saturated, six desatu-rated, and one white) was shown once at the threedifferent sizes so that 39 color-naming judgmentswere made by each person. The order of presenta-tion was randomized for each size.

Subjects were told that they would be presentedwith circular patches of different colors, includingsome neutral shades. They were instructed to iden-tify the colors as red, orange, yellow, green, blue, orpurple, or, if they could not detect any color, to call acolor black or white. A choice always had to be madewith this fixed set of names. The subjects were notinformed whether they were correct and were pre-sented with each stimulus only once. A new colorwas presented when a reply was given. Although noofficial time limit was imposed, no subject took longerthan 10 s to make a decision.

Each experimental naming trial lasted 30 min.In order to see if the period of adaptation or theambient level of illumination had any bearing onnaming capacity, three observers were retested 30min after their initial 30-min trial (i.e., 60-90 minafter the start of the session). No differences werefound during this retest, suggesting that the 5-minpreadaptation was adequate and that changes in theambient illumination had no effect on outcomes.

Results

Table 4 shows the calls (horizontal rows of colors) foreach color chip (vertical columns of colors) at the twolevels of saturation and three sizes. It is worth


0.8 I

10.7-

0.5-

0.4-

0.3-

0.2-

I

Table 4. Response Matrix for Both Levels of Saturation and Three Aperture SizesaSATURATED

LARGE

Red Ora Yel Gre Blu Pur Whi Bla

Red 13

Ora 2 11

Yel 8 5Gre 8 3 2

Pur 3 1 2 7

Whi 1 3 1 7

DESATURATED

LARGE


Red2 4 5 2

Ora 7 1 5

Yel 1 12

Gre 5 4 4

Pur 2 2 1 8

MEDIUM


Red- 11Ora 2 | l1

Yel9 4Gre 5 2 1 5

__u 3 -_

Pur 1 3 9

Whi= == 1 3 9

MEDIUM

Red Ora Yel Gre Blu Fur Whi Bla

Red2 4 5 2

Ora 1 9 3

Gre 1 5 4 3

Blu ~3 7 3

Fur 1 4 1 7

Wi 1 3 9

SMALL


|Red| 12 |4| | 1

Yel- 10 3

Gre 2 1 10

Bl 3 01 3;1

Fur 1 1 1 11

SMALL

Red Ora Yel Gre Blu ur Whi Bla

Red 1 5 4 3

Ora 5 8

Yell3 6 4

Gr 4 13 1 4Blu 1 33 6Fur 2 3 8

VWi I1 1 1

'The horizontal rows of colors identify the name categories used, whereas the vertical columns of colors list the chip color:Ora, orange; Yel, yellow; Gre, green; Blu, blue; Pur, purple; Whi, white; Bla, black.

considering the various possibilities represented bythis table.

If all colors had been correctly identified, thenumber 13 would appear along the diagonal with theother cells remaining empty. On the other hand, ifthere was no color recognition and each response wasequally likely, then the mean value for each cell wouldbe 13/8 ( - 2, being the total number of responses/thenumber of possible responses). Alternatively, if nocolor vision existed and only achromatic performancewas possible, then all cells, other than those in thewhite and the black columns, would be empty. Anyother distribution would indicate that either somecolor vision existed or that some bias toward calling acertain color name existed in the absence of anyapparent color. If bias were evident in the callingpattern of our observers then entries would be distrib-uted vertically down the column for which the biasexisted. We address the possibility of bias below.

Observation of these results shows that there is atendency for the chromatic responses to fall along thediagonal, as we would expect if there were some colorvision. However, because there is some spread aboutthe diagonal and some colors are called white or blackinappropriately, there must be a reduction in colordiscrimination. The large saturated red and orangecolors have a high correct recognition rate. The redcolor was recognized successfully on 26 out of 26presentations for the two larger sizes, and a slightlylower recognition rate was found for the smallest size(12/13). In contrast, the desaturated red and or-ange colors have a low recognition rate. Saturated

blue and green appear to have a moderate recognitionrate that is size dependent, showing poor perfor-mance at smaller sizes.

The statistical significance of these results wasconsidered following discussion with staff of theStatistical Consulting Centre at the University ofMelbourne, Melbourne, Australia. It was decidedthat treating these data as contingency tables was notvalid because the observations were not independent.Also, a chi-square test was not appropriate, as therewere a large number of empty cells in the contingencytables. Because each color was called by 13 observ-ers, who were unaware of the nature of the color setthat was going to be tested, we believe that the 13observations for each color are independent. There-fore a binomial test was considered as a valid andappropriate test for significance. We applied such atest to the distribution of the color names given ineach row of Table 4. In doing so, we adopt thehypothesis that colors cannot be recognized undermoonlight. Hence the frequency of random calls fora particular colored sample should have a binomialdistribution with N = 13 and p = 1/8, as the 13respondents could have given any one of eight an-swers for each color. We tested this hypothesis bycomparing the experimentally determined hit rateswith the expected binomial distribution. Such acomparison yields probability values for the numberof observed hits in each response category (Table 5).

The results of the binomial analysis are shown inTable 5, and from these data we can conclude that forthe two largest sizes (40 and 2°) all saturated colors


Red, red;

Table S. Probability of the Observed Number of Correct Calls for Each Color at Each Sizea

Large (40) Medium (2°) Small (0.50)

Prop. Prop. Prop.Color Corr. Prob. Corr. Prob. Corr. Prob.

SaturatedRed 1.000 0.0000 1.000 0.0000 0.923 0.0000Orange 0.846 0.0000 0.846 0.0000 0.769 0.0000Yellow 0.385 0.0030 0.308 0.0165 0.231 0.0690Green 0.615 0.0000 0.385 0.0030 0.000 0.8238Blue 0.538 0.0000 0.769 0.0000 0.231 0.0690Purple 0.154 0.2159 0.000 0.8238 0.077 0.4965White 0.538 0.0000 0.692 0.0000 0.846 0.0000

DesaturatedRed 0.154 0.2159 0.154 0.2159 0.077 0.4965Orange 0.000 0.8238 0.077 0.4965 0.000 0.8238Yellow 0.923 0.0000 0.846 0.0000 0.462 0.0004Green 0.385 0.0030 0.385 0.0030 0.077 0.4965Blue 0.538 0.0000 0.538 0.0000 0.231 0.0690Purple 0.077 0.4965 0.077 0.4965 0.000 0.8238White 0.538 0.0000 0.692 0.0000 0.846 0.0000

aBinomial distribution statistics were used: Prop. Corr., proportion correct; Prob., probability.

(except purple) were correctly recognized above chance In order to examine for bias in color naming, welevel (p < 0.05). For the smallest saturated colors, constructed 2 x 2 tables for each color and analyzedonly red and orange were still recognized above the results by using signal detection theory.10 Thischance (p < 0.05). Saturated blue, green, and yel- analysis should be used only as a guide, as the cellslow show a size effect, with poorer recognition at are based on interobserver responses obtained for thesmaller sizes. three target sizes. We define accuracy and bias as

For the desaturated colors, yellow, green, and blue follows:were correctly recognized above chance level for thetwo largest sizes, whereas at the smallest size, only Accuracy = (hits + correct rejections)yellow was correctly recognized above chance level. /total presentations,However, Table 4 indicates that the name yellow wasused with many other color chips, and therefore a Bias = -0.5 [z(H) + z(F)],substantial bias exists in the use of that name. Asimilar, although slightly less prominent, bias is also where z(H) is the z score of hits, and z(F) is the z scoreevident for green and blue. Hence the results of the of false alarms.hit rates for these desaturated colors should be The results of this analysis are shown in Table 6.interpreted with caution. The benefit of using this analysis is that accuracy can

Table 6. Proportion of Hits, False Alarms, and Correct Rejections for Both Saturated and Desaturated Colorso

False Correct BiasColors Hits Alarms Misses Rejection Accuracy c

SaturatedRed 0.97 0.04 0.03 0.82 0.90 -0.07Orange 0.82 0.46 0.18 0.97 0.89 -0.41Yellow 0.31 0.01 0.69 0.85 0.58 1.41Green 0.08 0.04 0.67 0.82 0.45 1.83Blue 0.01 0.08 0.46 0.79 0.40 1.87Purple 0.08 0.01 0.92 0.85 0.46 1.87White 0.69 0.08 0.31 0.79 0.74 0.45

DesaturatedRed 0.13 0.00 0.87 0.96 0.54 2.11Orange 0.03 0.08 0.18 0.92 0.47 1.64Yellow 0.74 0.19 0.26 0.81 0.78 0.12Green 0.12 0.06 0.72 0.94 0.53 0.19Blue 0.01 0.12 0.56 0.88 0.45 1.75Purple 0.32 0.01 0.95 0.99 0.65 1.40White 0.69 0.32 0.31 0.68 0.69 -0.01

aAlso given are the accuracy and the bias (c) statistics. Boldfaced values indicatep < 0.05.


be expressed in terms of hits and correct rejections.This is an important aspect of our study because it isuseful to establish that not only did the subjectscorrectly identify the color but also that they did notconfuse other colors for that color category. Perfectperformance should yield an accuracy index of 1.00.Bias, on the other hand, was calculated by the use ofthe response bias index, which is a measure of thesubject's willingness to use a certain color name.' 0

Positive bias values indicate that the false alarm rateis lower than the miss rate, whereas negative valuesindicate the opposite. A positive bias value meansthat the name was not used often, and as a conse-quence many misses occurred. Alternatively, a nega-tive value suggests that the color name was usedoften but inappropriately.

Table 6 offers support to our earlier binomialanalysis. The saturated red color is accurately iden-tified on some 90% of occasions. All other colorsshow a lower level of accuracy, with the saturatedorange, desaturated yellow, and white giving thehighest accuracy. However, the orange shows consid-erable bias, giving an overuse of that color categorywith saturated colors and a significant under use withdesaturated colors. Its accuracy may be affected bythis fact, and thus the accuracy values of orangeshould be interpreted cautiously. For the othercolors, saturated green, blue, yellow, and purple anddesaturated red, orange, purple, and blue all showconsiderable bias to not having their color namesused, and consequently have high miss rates. Thiswould suggest that these colors are not being recog-nized with any degree of reliability.

Discussion and ConclusionsIn our sample, red had the highest recognition rate,being 100% for the saturated large samples. This isconfirmed by its high accuracy and low bias in the useof the color name. The consistency between therecognition rate, accuracy, and bias suggests thatsaturated reds are reliably identified under full moon-light at levels beyond chance performance. We be-lieve that this finding is not surprising and is consis-tent with our introductory discussion regarding thephotochromatic interval. However, not all red col-ors have such a high recognition rate. We foundthat size and saturation affect the recognition of red.The recognition rate and the accuracy of desaturatedreds are low, and there is a bias to not use that namewith desaturated samples, which suggests that redsare not perceived at this low level of saturation.Likewise the recognition rate of red drops off withsmaller sizes.

For the other colors, orange showed a similar trendto red, but the bias for the overuse of its name makesthe interpretation of its data difficult. Perhaps theobservers were perceiving the longer-wavelength redcomponent of the orange, and hence, as the longerwavelengths are not as prominent as with the red,some reduced level of performance is to be expected.If this hypothesis were correct, then one could expect

that yellow would give the worst performance of thesethree colors as it has the least amount of longwavelengths present. The trend is consistent withthis hypothesis for saturated colors but reverses withthe desaturated colors for which yellow is recognizedreasonably accurately. We believe that this latterfinding may be another manifestation of the interac-tion between saturation and color perception men-tioned in the introduction. With red, desaturationhas a detrimental effect, by diluting the longer wave-lengths, whereas with yellow, desaturation has apositive effect, improving accuracy and reducing bias.How such desaturation alters the long-wavelengthcomponents of yellow is not apparent and requiresfurther consideration.

Green, blue, and purple were not recognized atother than low levels of reliability and there appearedto be a bias to underuse these color names. This isconsistent with the fact that subjects could notidentify the colors with any level of accuracy, andsubstantial confusion existed in their color naming.Green and blue showed a substantial size effect, beingless reliably recognized as smaller sizes, which isconsistent with the phenomenon of foveal tritanopia.3

Under full moonlight the recognition rate is notalways 100%, which indicates some reduction in colordiscrimination compared with that evident underphotopic conditions, which results in (a) confusionwith nearest-neighbor colors and (b) confusion withneutrals.

Such confusions are most pronounced for all colorsother than the large saturated red. We concludethat large saturated reds will be reliably recognizedunder full moonlight. We believe that the long-wavelength-sensitive cones mediate this detectionand are responsible for the detection of the long-wavelength components of orange and yellow as well.

In conclusion, the results of this research indicatethat people with normal color vision have some colordiscrimination under full-moon light, particularly forred colors. However, the limited scope of this experi-ment and the lack of independence of some observa-tions limit the reliability of the conclusions that wecan draw from the bias analysis.

We thank Rob Hyndman of the Statistical Consult-ing Centre at the University of Melbourne for adviceon the statistical analysis of the results and anunidentified reviewer for advice regarding statisticalanalysis.

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