Chromatic Illumination Discrimination Ability Reveals that Human Colour Constancy Is Optimised for Blue Daylight Illuminations

Abstract

The phenomenon of colour constancy in human visual perception keeps surface colours constant, despite changes in their reflected light due to changing illumination. Although colour constancy has evolved under a constrained subset of illuminations, it is unknown whether its underlying mechanisms, thought to involve multiple components from retina to cortex, are optimised for particular environmental variations. Here we demonstrate a new method for investigating colour constancy using illumination matching in real scenes which, unlike previous methods using surface matching and simulated scenes, allows testing of multiple, real illuminations. We use real scenes consisting of solid familiar or unfamiliar objects against uniform or variegated backgrounds and compare discrimination performance for typical illuminations from the daylight chromaticity locus (approximately blue-yellow) and atypical spectra from an orthogonal locus (approximately red-green, at correlated colour temperature 6700 K), all produced in real time by a 10-channel LED illuminator. We find that discrimination of illumination changes is poorer along the daylight locus than the atypical locus, and is poorest particularly for bluer illumination changes, demonstrating conversely that surface colour constancy is best for blue daylight illuminations. Illumination discrimination is also enhanced, and therefore colour constancy diminished, for uniform backgrounds, irrespective of the object type. These results are not explained by statistical properties of the scene signal changes at the retinal level. We conclude that high-level mechanisms of colour constancy are biased for the blue daylight illuminations and variegated backgrounds to which the human visual system has typically been exposed.

Full text

Chromatic Illumination Discrimination Ability Reveals
that Human Colour Constancy Is Optimised for Blue
Daylight Illuminations
Bradley Pearce
1
*, Stuart Crichton
1
, Michal Mackiewicz
2
, Graham D. Finlayson
2
, Anya Hurlbert
1
1Institute of Neuroscience, Faculty of Medical Sciences, Newcastle University, England, United Kingdom, 2School of Computing Sciences, University of East Anglia,
Norwich, England, United Kingdom
Abstract
The phenomenon of colour constancy in human visual perception keeps surface colours constant, despite changes in their
reflected light due to changing illumination. Although colour constancy has evolved under a constrained subset of
illuminations, it is unknown whether its underlying mechanisms, thought to involve multiple components from retina to
cortex, are optimised for particular environmental variations. Here we demonstrate a new method for investigating colour
constancy using illumination matching in real scenes which, unlike previous methods using surface matching and simulated
scenes, allows testing of multiple, real illuminations. We use real scenes consisting of solid familiar or unfamiliar objects
against uniform or variegated backgrounds and compare discrimination performance for typical illuminations from the
daylight chromaticity locus (approximately blue-yellow) and atypical spectra from an orthogonal locus (approximately red-
green, at correlated colour temperature 6700 K), all produced in real time by a 10-channel LED illuminator. We find that
discrimination of illumination changes is poorer along the daylight locus than the atypical locus, and is poorest particularly
for bluer illumination changes, demonstrating conversely that surface colour constancy is best for blue daylight
illuminations. Illumination discrimination is also enhanced, and therefore colour constancy diminished, for uniform
backgrounds, irrespective of the object type. These results are not explained by statistical properties of the scene signal
changes at the retinal level. We conclude that high-level mechanisms of colour constancy are biased for the blue daylight
illuminations and variegated backgrounds to which the human visual system has typically been exposed.
Citation: Pearce B, Crichton S, Mackiewicz M, Finlayson GD, Hurlbert A (2014) Chromatic Illumination Discrimination Ability Reveals that Human Colour
Constancy Is Optimised for Blue Daylight Illuminations. PLoS ONE 9(2): e87989. doi:10.1371/journal.pone.0087989
Editor: Daniel Osorio, University of Sussex, United Kingdom
Received August 7, 2013; Accepted January 2, 2014; Published February 19, 2014
Copyright: ß2014 Pearce et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This project was part of a larger project funded by the Engineering and Physical Sciences Research Council (EPSRC), grant number: EP/H022236/1. The
funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: b.m.pearce@ncl.ac.uk
Introduction
Evidence suggests that the human visual system is optimised for
the environment in which it evolved, particularly at retinal and
thalamic levels where spatial and spectral sensitivities have been
shown to be tuned to natural scene statistics [1–4]. Although the
ecological theory of perception would also suggest that higher
cortical mechanisms are sculpted by natural scene statistics
through evolutionary pressure [5], there is less direct evidence
for such optimisation, particularly for mechanisms underlying
colour perception. Colour constancy – the phenomenon by which
object colours are perceived as constant despite changes in the
illumination spectrum – is thought to involve mechanisms at the
higher cortical level, in addition to retinal and thalamic
components [6–9]. Here, we examine the hypothesis that colour
constancy mechanisms per se are optimised for natural environ-
ments, and in particular, for natural illuminations.
The natural illuminations under which humans evolved are
defined by the daylight locus, which describes the chromaticities of
regular and typical variations of sunlight due to time of day, cloud-
cover and geographical location, and closely parallels the
chromaticities of black-body radiation at varying temperature, or
the Planckian locus [10]. In industrial times, humans have also been
exposed to manufactured light sources, including candlelight and
incandescent lamps, and, most recently, fluorescent and solid-state
light sources that have been designed to emulate neutral daylight
illuminations [11]. When the illumination on a particular surface
changes, the spectrum of light reflected from the surface also
changes, although its intrinsic reflectance properties do not. In
colour constancy, the human visual system has evolved mecha-
nisms to keep surface colours constant across changes in the
illumination, maintaining perception that closely corresponds to
the unchanging surface reflectance properties rather than to
variations in the reflected light [12].
Previous experiments investigating colour constancy have tested
participants’ ability to judge changes in colour appearance of
uniform patches in scenes, under a small number of distinct
illuminations [13–16]. These experiments have extensively probed
mechanisms of colour constancy, but in general, with few
exceptions [16], the experimental aims were not to elucidate
under which illuminations these mechanisms perform best.
The surfaces used in colour constancy experiments are usually
either simulated, using computer monitors, or are made from
controlled paper with uniform chromaticities [17], with a few
exceptions, in which real scenes have been shown under a small
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number of illuminations produced by a small number of fixed
primary lamps [13,18,19], which are not representative of the
natural range of global illuminations to which we are usually
exposed. (In some of these experiments, additional spot lamps
were used to illuminate a target surface only.) Where real scenes
are used, these are also typically composed of generic, unfamiliar
objects.
Although there is evidence that colour constancy improves as
the number of surfaces within a scene increases [20–22], the
notion that more complex characteristics of natural scenes may
contribute to colour constancy – for example, via the memory
colour of familiar objects providing a reference surface for colour
calibration [12] – has not been adequately tested. Recent
experimental evidence is divided, demonstrating heightened
colour constancy for colour matches of Munsell papers in real
scenes containing (among other fruits) a banana [19] but no effect
of an image of a real banana image on colour matches of
simulated patches [23]. These experiments address surface colour
specifically, and while it is not clear whether colour constancy
mechanisms are optimised for frequently encountered or natural
surface colours, it is also unclear whether constancy mechanisms
are biased towards illuminations to which we are commonly
exposed.
Instead of matching colours of objects or surfaces under
changing illuminations, here we introduce a new method of
quantifying colour constancy using forced-choice illumination
matching. In this method, observers first view a reference scene
and then select from two successively presented scenes the one in
which the illumination matches that of the reference scene. The
surfaces and their spatial configuration are unchanged between the
reference and alternatives; only the illumination changes. By
systematically varying the illumination difference between the two
alternatives, we obtain an illumination discrimination curve for
each reference illumination. The rationale underlying this task as a
measure of surface colour constancy is the same as that underlying
asymmetric surface matching task measures [24,25]. In the latter,
observers typically adjust the chromaticity of a surface patch under
a reference illumination to match its appearance under a test
illumination. If the observer were perfectly colour constant, he
would perceive as identical the two different chromaticities elicited
by a fixed surface reflectance under two different illuminations. In
practice, colour constancy is not perfect, and the matching
chromaticity deviates from that predicted for a fixed surface
reflectance. This deviation is typically cited as incomplete
compensation for the change in illumination and therefore
measures the lower limit of colour constancy under a fixed,
typically large illumination change. Here, we instead measure the
upper limit of colour constancy under varying illumination
changes, by holding surface reflectances fixed and determining
the range of illumination changes under which they indeed retain
the same appearance. If an observer is unable to perceive a change
in scene appearance under changes in illumination, then he is
perfectly colour constant. If, conversely, the observer perceives a
change in scene appearance and is therefore able to discriminate
between illuminations, she is not perfectly colour constant.
It is important to note that, unlike in ‘‘operational colour
constancy’’ studies [26], we are not measuring the ability of the
observer to attribute a change in scene appearance correctly to a
change in illumination versus a change in surface material, but
instead measuring the ability of the observer to determine whether
a change in scene appearance has occurred, explicitly under a
change in illumination only. This method of illumination
matching therefore probes colour constancy at the sensory level
of appearance rather than a higher level of cognitive judgment.
Because in the natural world, illuminations change more
frequently than surface reflectances, this task provides a natural
assessment of the limits of constancy: the limits of illumination
change under which the visual system perceives no change in
scene appearance.
We measure discrimination curves for systematically controlled
changes in illumination, generated by a spectrally tuneable multi-
channel LED light source, on real 3D surfaces (Figure 1A). The
illuminations we use have broadband spectra and are either 1)
metamers of daylight illuminations, or 2) atypical illuminations
that share a correlated colour temperature with a central point on
the daylight locus. Based on the premise that better illumination
discrimination indicates poorer colour constancy, we test the
following hypotheses: firstly, that illumination discrimination for
atypical illuminations will be greater than for daylight illumina-
tions; secondly, that illumination discrimination for scenes with a
single uniform background surface will be greater than for those
with multiple distinct surfaces; and third, that the presence of
objects such as fruit, which have coevolved with human colour
vision [3], will cue colour constancy mechanisms more effectively
than chromatically matched, novel objects, and that therefore,
illumination discrimination will be poorer for equivalent illumi-
nation changes on these scenes.
Methods Overview
Participants were presented on each trial with a reference
(target) illumination that illuminated a viewing box containing one
of six scenes, with one of three scene content types (fruits – a real
apple, banana, and a realistic fake pear; novel objects – three
distinct 3D paper shapes with matched surface colours to the
fruits; or no objects) and one of two backgrounds (uniform grey or
Mondrian) (Figure 1B, 1C). Shortly after the target illumination
had been presented, two test (comparison) illuminations were
presented, successively, one of which was always identical to the
target illumination, in a two-alternative forced choice task.
Participants signalled on each trial which of the two comparison
illuminations was the closest match to the target illumination. The
target illumination was presented for 2000 ms and the compar-
isons each for 1000 ms with a 400 ms dark period separating each
illumination. The difference between the target and comparison
illuminations was systematically varied between trials to determine
thresholds for illumination discrimination.
Illumination chromaticities varied along two distinct loci: the
Commission Internationale de l’E
´clairage (CIE) daylight locus and
an orthogonal, atypical locus. The daylight locus closely parallels
the Planckian (blackbody radiation) locus and varies from
correlated colour temperatures of approximately 40000 K (blue-
ish) to 4000K (yellow-ish) (Figure 1D). The atypical chromaticities
were taken from the isotemperature line at 6700 K, which by
definition is perpendicular to the Planckian locus in the uniform
chromaticity plane at that point, computed according to the
method established by Mori et al. (in Wyszecki and Stiles [10]).
(Note that because of the way in which isotemperature lines are
defined, they are of necessity not perpendicular to the Planckian
locus when plotted in a non-uniform colour space, as in Figure 1D.)
Chromaticities on this orthogonal curve varied along a roughly
greenish-reddish (or cyan-magenta) axis. Two target chromaticities
were selected on each locus, at 610 perceptual steps (CIE DE
uv
units) from D67 in the CIE Lu*v* colour space (see Figure 1D for
a plot of all the generated chromaticties in CIE 1931 Yxy colour
space, atop daylight measurements). For trials in which both
comparison illuminations were the same as the target illumination
(60DE
uv
from target), one of the comparison intervals was
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arbitrarily pre-designated to be the correct choice, and therefore
performance was expected to be at chance as the observer should
be equally likely to pick either one of the two identical comparison
illuminations. Performance data for these trials were indeed not
significantly different from chance. Performance for comparison
illuminations 658 DE
uv
from the target illumination, in which one
comparison is identical to the target and the other an extreme
change, did not differ from 100%. Therefore both of these trial
types were removed from the statistical analysis; nonetheless,
performance on these trials demonstrates that the task is
meaningful and that observers comprehend its demands.
Results and Discussion
Illumination discrimination thresholds vary with
chromatic direction and scene background
A repeated-measured ANOVA with three independent vari-
ables was used to analyse the data. The results demonstrate a
significant performance difference between the daylight and
orthogonal loci (F(1,7) = 17.404 p,.01), with mean discrimination
accuracy (percent correct) lower for daylight illuminations (70.20%
vs 74.74%), and mean accuracy across all illuminations and
conditions equal to 72.47%. Mean discrimination accuracy for the
grey backgrounds (m= 76.37%) is significantly higher than for
Mondrian backgrounds (m= 68.57%; F(1,7) = 11.385, p = .012)
(Figure 2A). No significant difference in discrimination accuracy is
found for the different scene contents conditions: fruit, novel or no
objects (F(2,6) = 1.776, p = .248).
For finer analysis of the illumination discrimination patterns, we
divided each locus into two parts by splitting each locus at the
center point (D67), thereby creating four loci of chromatic
directions: bluer, redder, greener and yellower illuminations. A
subsequent repeated-measures ANOVA with Greenhouse-Geisser
corrections shows that over all conditions, mean accuracy differs
significantly between chromatic directions (F(2.12,14.85) = 15.031,
p,.01; Figure 2B). Illumination discrimination is poorest for bluer
changes and most accurate for greener changes. Post-hoc tests
using Tukey’s HSD test shows performance on all chromatic
directions to be significantly different between the Mondrian and
grey background conditions (p,.05) with the exception of the
greener illuminations; greener illuminations are, though, signifi-
cantly different from bluer illuminations in each background
condition separately (p,.01), and significantly different from the
other chromatic directions (p,.05), while redder and yellower
illuminations are not significantly different from each other but are
from both bluer and greener illuminations (p,.05).
Scene Statistics do not Predict Illumination
Discrimination Asymmetries
Certain computational theories of colour constancy [6,8]
assume either that the scene surface reflectances average to
neutral or that the brightest surface is white, thereby enabling an
estimate of the illumination chromaticity to be gained from scene
statistics. If scene statistics are the sole contributors to constancy
mechanisms, we may expect their variation to explain the
variation in performance under different illuminations that we
observe here. For example, if the brightest-is-white strategy
governs illumination estimation, we would expect discrimination
performance to be greatest for those illumination changes in which
there is greatest change in the visual signal from the brightest
surfaces in the scene. We therefore examined in further detail the
distributions of illumination change signals conveyed by the
reflected light from surfaces across the entire box. To do so, we
first took hyperspectral images of the grey and Mondrian box
backgrounds under each of the 34 unique test illuminations
(sampling the spectra at 4 nm intervals at each pixel in an image
array of 19176800 pixels), then selected 95 distinct patches at
random in the Mondrian background image and analysed the
spectra from these locations and from the exactly corresponding
spatial locations in the grey background images. Spectra within
each patch were averaged and converted to cone excitations. For
each patch and each test illumination, the change in cone
Figure 1. Photographs of the illuminator equipment and the
scene backgrounds, with a plot of the chromaticity coordinates
of illuminations used in the experiment. A. Photograph of
illuminator and the viewing box (with front wall removed) under
extreme blue illumination, with fake pear, banana and chromatically
matched novel objects. B. The Mondrian background used for the
variegated scene condition, under D67 illumination. C. The grey
background used for the grey scene condition. D. Chromaticities of
generated metamers atop daylight measurements taken and digitised
from Hernandez-Andres et al. [36], in CIE 1931 colour space; green
markers show chromaticities of Ugandan forest canopy illuminations
measured by Sumner and Mollon [2].
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excitation elicited under the test illumination relative to the target
illumination was computed in each of the three channels of
luminance (L+M), red-green (L2M), and blue-yellow (S 2[L+M])
in the modified MacLeod-Boynton (McB) cone-opponent contrast
space [27–29] (see the description of cone contrast calculation in
the Methods).
Histograms of the McB channel changes are illustrated in
Figure 3A, for the test illuminations at distance 18 DE
uv
from the
target illumination in each of the four chromatic directions, for the
Mondrian background (for the grey background see Figure S1, in
supplementary information). There is no visible cue from the
shape or magnitudes of the change signal that would explain the
asymmetries in performance between the chromatic directions; in
particular, neither the maximum nor mean signal in any of the
three channels is greater for the greener illumination change than
for the other illumination change directions. (See Figure S2B,
which explicitly compares maximum and mean changes, as well as
skewness and kurtosis of the change distributions, for each
illumination direction across all change increments, in the
luminance channel). Statistical analysis confirms that there is no
significant correlation between any of these characteristics (in any
McB channel) and discrimination performance for that chromatic
direction alone. For example, maximum luminance change for
yellower illuminations does not correlate with mean performance
for those illumination changes, but does correlate highly with
performance for greener illuminations (r = .884, p,.05); more-
over, the maximum luminance change for bluer illuminations
correlates with performance on all but redder illuminations
(r = .979, .960, .977; p,.05, for yellow, blue and green illumina-
tion changes respectively). Therefore, neither the maximum nor
mean McB changes account for performance in any specific
chromatic direction, or explain the observed chromatic biases, and
therefore neither does the brightest-is-white assumption Further-
more, the possibility that observers are adopting the strategy of
monitoring signal changes in a single Mondrian patch assumed to
be white or neutral is excluded because (a) the Mondrian pattern
deliberately contains no patches of neutral reflectance; (b) the
pattern of McB changes between patches across illumination
directions is highly variable, so that the observer would be unable
to predict the identity of the brightest patch from trial to trial; and
(c) the asymmetry in performance holds for the grey background,
effectively a single patch, and is also unexplained by its distribution
of McB channel changes.
Changes in the average scene chromaticity also do not explain
performance differences between the Mondrian and grey back-
grounds. Neither condition satisfies the grey world assumption
[6,30,31]: the average scene chromaticity is not an accurate
predictor of the scene illumination chromaticity for either
background. The means of the 95 surface chromaticities are
shown in Figure 3B. The scene average chromaticities are shifted
relative to the illumination chromaticities, although the DE
uv
intervals and relative positions of the test illuminations are roughly
preserved. For both backgrounds, this shift is explained by the
average surface reflectance not being perfectly neutral; in
particular, the grey paper reflectance is slightly higher in the
short-wavelength region compared to the long (as in figure S2A).
As the number of surfaces in the scene increases, the distribution of
mean chromaticities tightens, but not sufficiently to explain the
difference in performance between Mondrian and grey back-
grounds: for equivalent changes in mean scene chromaticity in the
two backgrounds, performance is still significantly greater for the
grey background (F(1,29) = 51.692, p,.001; repeated-measures
ANOVA calculated from the interpolated performance curves as a
function of box mean chromaticity under each comparison
Figure 2. Mean discrimination accuracy for various conditions.
A. Mean discrimination accuracy for illuminations by their chromatic
direction, for conditions using the grey or Mondrian background; for
significant differences see main text. B. Mean accuracy across all
conditions and participants for each chromatic direction as a function of
perceptual distance from the target chromaticity DE
uv
. C. Computed
DE
uv
mean thresholds at 75% accuracy for each chromatic direction,
plotted in CIE u*v* colour space, with a spline forming the just-
noticeable-difference discrimination contour (bold line) from D67 (black
marker); just-noticeable-difference MacAdam ellipse boundary for D65
(dashed line) plotted around D67 point.
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Colour Constancy by Illumination Discrimination
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illumination). Moreover, performance (again as a function of box
mean scene chromaticity) depends significantly on illumination
direction (F(1.51, 43.86) = 77.318, p,.001), with post-hoc t-tests
(performed as paired sample t-tests in which the interpolated
performance on each illumination direction for the Mondrian
condition was paired with the performance of that illumination
direction for the grey condition) confirming that yellow, red and
green illuminations are significantly different across background
conditions (t(29) = 4.70, t(29)= 9.46, t(29) = 8.58, p,.05, respec-
tively). The difference in performance between the Mondrian and
grey backgrounds is therefore more likely to be due to the surface
variegation itself. Moreover, the bias against illumination discrim-
ination in the bluer direction and towards illumination discrim-
ination in the greener direction is not explained by systematic
differences in the mean scene chromaticity changes along the bluer
or greener directions (see Figure 3B).
Surface Chromatic Discrimination Thresholds do not
Predict Illumination Discrimination Asymmetries
Asymmetries in chromatic discrimination for colour patch
matching tasks are well described by discrimination ellipses
[10,32,33], which illustrate just noticeable differences from a
central chromaticity at each point along the ellipse contour, with
chromaticities falling inside of the ellipse indistinguishable from
the centroid chromaticities. It is natural to ask whether these
surface chromaticity discrimination asymmetries explain the
asymmetries in global illumination discrimination. We therefore
compare the standard MacAdam ellipse [10] in this region of the
chromaticity diagram with the approximate ellipse computed from
the mean discrimination thresholds of observers for this task, in
Figure 2C, (threshold values in DE
uv
units: green 10.7; red 17.9;
blue 25.7; yellow: 18.0). (Note that in the perceptually uniform
CIE Lu*v* space the MacAdam ellipse becomes roughly circular.
Also, because there is no standard ellipse centred on the D67
chromaticity, we have re-centred the D65 ellipse on the D67
chromaticity (u*v* = 24.905, 7.061), which sits at the just-
noticeable-difference border of the D65 ellipse). The discrimina-
tion thresholds for the illumination discrimination task are much
larger than the MacAdam ellipse, and asymmetric between the
axes as well as between the unipolar directions of each axis. The
general magnitude difference between the illumination judgment
thresholds and the MacAdam ellipse is at least partly explained by
the task differences: in this task, comparison illuminations are
presented successively rather than simultaneously as is the case for
patches in colour field matching experiments (e.g.Krauskopf and
Gegenfurtner [32]), and the discrimination is global rather than
local. While empirical results and models of chromatic discrim-
ination of chromatically variegated surfaces [34,35] suggest that
elongation of discrimination ellipses (reduced sensitivity) occurs
along directions of maximal chromatic variation within stimuli, the
reduced sensitivity along the blue-yellow axis in this task cannot be
explained by a bias in chromatic variation of the background or
scene surfaces, as these vary significantly between the Mondrian
and grey backgrounds (see Figure S3 in the supplemental
information for the principal axes of chromatic variation), and
the performance bias across illuminations is the same for both.
General Discussion
In paradigms that use surface colour matching across illumina-
tions to measure colour constancy, close matches to a target
patch’s surface spectral reflectance function require the visual
system to discount the scene illumination; in the case of
achromatic adjustment tasks, a perfect match would result in the
patch appearing white while having the same chromaticity as the
scene illumination [17]. We therefore propose that colour
constancy may be measured using an illumination discrimination
task for fixed surface reflectances, with poor discrimination of
changes in scene illumination signalling conservation of scene
appearance and therefore good colour constancy and, conversely,
high change discrimination signalling poor constancy. That is, if
the observer is unable to perceive a change in surface colour
appearance under changes in illumination, he is perfectly colour
constant. Illumination discrimination was poorest for bluer
illuminations along the daylight locus, indicating heightened
colour constancy for these illuminations over all others. Poorest
colour constancy is experienced in the greener illuminations along
the orthogonal locus, for which discrimination between illumina-
tions was best.
The results demonstrate clear differences between chromatic
directions, with the least typical illuminations eliciting the best
discrimination. Bluish illuminations are the most common among
daylight illuminations, followed by yellowish illuminations, then by
the rarer reddish illuminations experienced near sunset [36], and
lastly by greenish illuminations, experienced only in scenes with
dense forestation [28,37], and displaced from the daylight locus as
demonstrated by measurements from the Ugandan forest canopy
[2] (see Figure 1D). The accuracy of illumination discrimination
follows this pattern, with illumination changes that are more
common in nature discriminated less effectively. The asymmetry
within and between axes suggests a bias that is not seen in surface
colour discrimination. Other studies of colour constancy have
reported chromatic direction biases; for example, better colour
constancy is reported for illumination shifts in the blue-yellow
direction compared to shifts in the red-green direction [25] , in an
asymmetric surface matching task, partly explained by a cone-
opponent adaptation model, but demonstrated only for a small
number of fixed shifts in unnatural illuminations (mixed narrow-
band) and without systematic exploration of chromatic axes.
Accelerated chromatic adaptation to greenish shifts in surface
colours of heterochromatic stimuli at very short time scales, as
measured by the shift in corresponding achromatic point [38], has
also been reported. These results suggest differences in the
dynamics of chromatic adaptation between chromatic directions
and are generally consistent with ours in demonstrating improved
performance in the greenish direction, but imply the opposite
consequence for colour constancy. The difference in methodology
between these studies and ours precludes further detailed
comparison. Moreover, although surface discrimination studies
also find evidence for higher blue-yellow thresholds (an elongated
blue-yellow axis), and enhanced discrimination along the red-
green axis [32,39], as shown in our data, this is the first evidence
for enhanced change discrimination specifically in the green
direction and not mirrored in the red direction.
Figure 3. Histograms of changes in cone-opponent channel excitations of 95 surfaces between D67 and the bluer, redder, greener
and yellower illuminations ±18DE
uv
away in the Mondrian background condition, in modified MacLeod-Boynton (McB)
coordinates. B. Mean scene chromaticities under each comparison illumination for one target illumination (grey symbol), for the daylight (blue
symbols) and orthogonal (green symbols) loci, in CIE 1931 xy chromaticity coordinates. Illumination chromaticities are also shown (black symbols).
Left: grey background condition. Right: Mondrian condition. Note that targets (lighter markers) are asymmetrically placed with respect to the crossing
position.
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The results also indicate significantly poorer illumination
discrimination, and therefore stronger colour constancy, for the
variegated (Mondrian) background relative to the uniform grey
background, across all chromatic directions. Previous studies using
self-luminous computer displays have demonstrated that as the
number of surfaces with distinct chromaticities presented to the
viewer increases – in other words, as the scene articulation
increases – so does the viewer’s ability to attribute changes in
surface chromaticity correctly to a (simulated) illumination or
surface reflectance change [22,38,40]. In the current task, the
viewer is required to make all judgments solely on the basis of an
illumination change; that is, the observer is informed explicitly that
only the illumination will change, and is aware that the
configuration and surface reflectances of patches in the Mondrian
scene do not vary across illuminations. The difference in
performance between the grey and Mondrian backgrounds
suggests that as the scene articulation increases, colour constancy
improves because illumination changes become less discriminable,
not because more information about the illumination per se is
available from the greater number of surfaces. The differences in
accuracy per chromatic direction are nonetheless preserved in
both conditions, which suggests a universal bias that is preserved
across scene contents. Crucially, this bias, perhaps affected by
levels of articulation in a scene, is independent of the surface
qualities within a scene.
Contrary to the hypothesis that the presence of real, familiar
objects will drive colour constancy mechanisms more effectively
than chromatically matched, novel objects, we found no significant
difference in performance for scenes containing fruits in compar-
ison to novel objects, across all illumination and background
conditions. This lack of a familiarity effect might be due to the
information articulated by the local surround outweighing that
from the fruits or novel objects, for both the grey and Mondrian
backgrounds. We suggest that silencing the background signal or
focussing attention on the object itself may be necessary to reveal
an effect of object familiarity, and are therefore examining this
possibility in further experiments.
Contrary to certain computational models of colour constancy
[6,8], low-level image statistics do not explain the illumination
discrimination performance, as demonstrated by analysis of the
signals available to the initial cone-opponent contrast encoding
pathways, obtained from hyperspectral images of the entire scene
under the varying illuminations. In particular, there is a significant
performance bias for blue illumination, with no corresponding bias
in the statistics-based signals. It is therefore difficult to explain the
performance differences between chromatic directions in terms of
statistics-based signal processing at early levels in the visual
pathway. The results instead lend weight to the notion that higher-
level cortical mechanisms contribute significantly to colour
constancy and that these are optimised for the natural environ-
ment. This conclusion is broadly consistent with other reports of
optimisation and bias at higher levels in the visual pathway. For
example, dichoptic presentation of scenes has been shown to affect
levels of chromatic adaptation, placing at least some of the
underlying mechanisms in the cortex [38]. Early cortical
organisation of colour and orientation processing has also been
shown to reflect the statistical properties of natural images [5]. It is
also consistent with the notion that the visual system has been
shaped by colours in natural scenes to which we have been
exposed. The primary axis of variation in colour signals from
natural images falls along the blue-yellow axis in modified McB
space, for earth and sky images [29,41]. This variation is, in turn,
likely to arise largely from variations in natural illumination along
the blue-yellow daylight locus [29]. The visual system may
therefore benefit from silencing responses to typical blue-yellow
variations in favour of heightened discrimination for atypical
changes along the red-green axis, which are more likely to
correspond to changes in objects rather than illuminations. In
embedding this bias towards illumination chromaticities (blue
rather than green) to which it has typically been exposed during
human evolution, the visual system thus gains the ability to
distinguish between meaningful and non-meaningful variations in
the environment.
Methods
Ethics Statement
The experiment was conducted in accordance with the APA
Ethical Principles, and was granted ethical approval by the Ethics
Committee of the Faculty of Medical Sciences at Newcastle
University (reference number 00312). Participants were asked to
give written consent before participating in the study, and were
informed of their right to withdraw at any time, without penalty.
Participants
Eight observers (6 female; mean age 26 y; range 20–28)
participated in the study, all naı
¨ve to its purposes. All participants
were recruited by opportunity sampling through the Institute of
Neuroscience Research Volunteer Program on a first-come, first-
serve basis. All participants had normal or corrected to normal
visual acuity, and no colour vision deficiencies, as confirmed by
testing with the Ishihara Colour Plates and the Farnsworth-
Munsell 100-Hue Test (mean total error score 25 [42]).
Participants were paid £7 per hour for their participation in the
study, at the end of each experiment session.
Design
A two-alternative forced-choice task was used in a 26263
repeated-measures design. The independent variables were the
illumination sets (locus type: daylight or atypical) that illuminated
the viewing box, and the contents of the viewing box, which was
lined with either Mondrian or grey card, and contained either no
objects, fruits or novel objects.
Apparatus
A spectrally tuneable illuminator was used, consisting of 6 LED
(Gamma Scientific RS5B) light sources, each with a bank of 10
programmable LED channels, which project into an integrating
sphere, which in turn emits the combined light into a viewing box,
producing diffuse, nearly uniform illumination onto the contents of
the box [43]; see Figure 1A. The viewing box was 71 cm
(width)677 cm (depth)647 cm (height), with a viewing aperture of
7.5 cm height and 14.5 cm width built into the front wall of the
box, situated centrally 9.5 cm from the top of the box. A gaming
pad was linked to a computer running Windows 7, MATLAB
2011b and custom software, which also controlled the illuminator.
The computer was equipped with an ASIO enabled sound card, to
provide low-latency audio, which was outputted to headphones.
Stimuli
The viewing sides, back wall, and floor of the viewing box were
lined with either standard uniform matte grey poster board (with
mean CIE 1931 coordinates x = 0.299, y = 0.324, under the D67
illumination), or Mondrian paper (x = 0.321,y = 0.359, under D67;
see Figure 1, C & B respectively), and contained either no objects,
fruits (an apple, banana and realistic fake pear), or novel 3D
primitives constructed from paper card (see Figure 1A for
example). The Mondrian paper was inkjet-printed on non-glossy
Colour Constancy by Illumination Discrimination
PLOS ONE | www.plosone.org 7 February 2014 | Volume 9 | Issue 2 | e87989

paper. The Mondrian patches varied in size from 0.2 cm–
12.0 cm, or roughly 7.6 degrees of visual angle for the largest
patch size at the viewing distance of 90 cm. The paper surfaces of
the primitives were printed with an all-over multi-coloured
random squares pattern, in which the individual square colours
were colorimetrically matched to the real fruit surface colours
under D67 illumination (a cube matched with an apple, a
triangular prism with a banana and a pyramid with the pear),
using a calibrated ink-jet printer (see Table S2 for tabulated
chromaticities). Hyperspectral reflectance data of the background
surfaces are available from the corresponding author on request.
Two sets of illuminations – 17 samples each from the daylight
locus and an orthogonal locus – were created (see following
section). The chromaticities of the 2 target illuminations on each
locus were 610 perceptual steps (DE
uv
units) from D67 in the CIE
Lu*v* colour space (see Figure 3B). The chromaticities of the 11
comparison illuminations were 60, 6, 12, 18, 24 or 58 DE
uv
from
each target, as described in the main text.
Illumination Generation, Measurement and Calibration
To generate the illuminations, a set of chromaticities for the
target and test illuminations, separated by the desired DE
uv
intervals (as above), were selected from the two loci. The spectral
power distribution of each type of LED at 11 different intensities
(1% and 10–100% in steps of 10) was measured inside the
illuminator’s integrating sphere using a PR650 spectroradiometer.
These readings were used to produce a set of calibrated basis
functions, which were in turn used to calculate the closest
achievable matching illumination using the colorimetric match
method we have previously described [43]. This method
compensates for the intensity-dependent peak-wavelength shift
exhibited by each LED channel in the Gamma Scientific RS-5B
lamps, and seeks illumination spectra whose shape matches the
desired spectrum shape in the least-squares error sense and whose
CIE chromaticity coordinates precisely match the chromaticity of
the desired spectrum. This method is possible for the daylight locus
for which standardised spectra exist, but not for the orthogonal
locus. We therefore imposed an additional constraint of maximal
smoothness for the matching spectra on both loci. The final
constraint imposed was for constant overall luminous flux across
all illuminations.
To implement these constraints, we adapted the metamer sets
approach from Finlayson and Morovic [44]. Metamer sets were
computed for each desired chromaticity using linear models for
the LED channels at each of several intensity ranges. To select the
smoothest metamer for each chromaticity, quadratic program-
ming was used to find the convex combination of the spectra at
vertices of the metamer set convex hull whose smoothness is
maximal.
The resulting spectra for the most extreme chromaticity changes
are shown in Figure S2. The constant luminous flux constraint was
well met: the measured luminance of a fixed position in the white
integrating sphere varied less than 0.46% around a mean of 78.34
cd/m2 across all 34 illuminations. The luminance of a white
calibration tile inside the viewing box varied between 22.49 and
23.85 cd/m2 across all 34 illuminations (see Table S1 for
tabulated chromaticities). Repeated spectroradiometric measure-
ments of the LED channel basis functions and the 34 test
illuminations taken during and after the experimental sessions
ensured that the desired spectra were maintained; measurements
of the full metamer set showed a mean change of 1.19 DE
uv
over
the 6 weeks of testing.
General procedure
Participants were seated in front of the viewing box and asked to
look through the viewing aperture. Their heads were not fixed, but
their viewing distance from the scene was constrained by the box
front, which contained the viewing hole. The scene was not
initially visible as the box was not illuminated. Participants were
given standardised instructions for the experiment, and were
directed towards two marked buttons on the gaming pad that
signalled either 1 or 2. Participants were asked to use these to
indicate which of 2 lights shown was the closest match to the initial
light shown in each trial. The instructions read: ‘‘You will be shown a
light that illuminates the viewing box; this is the target light. Then there will be
two subsequent lights, you are asked to signal which is most like the target light,
using either of the buttons, [1]denoting the first light is most similar, or [2]for
the second light’’. A 2-minute dark adaptation period preceded the
start of the main experiment.
Each trial began with three audible tones delivered through the
participant’s headphones, signalling the start of a new trial. The
box was immediately illuminated by the selected target illumina-
tion, which remained on for 2000 ms. The illumination was
switched off and the box remained dark for 400 ms, before
another tone signalled the first comparison illumination which
illuminated the viewing box for 1000 ms. The box then went dark
for a further 400 ms before another tone signalled the second
illumination which illuminated the viewing box for 1000 ms. One
of these two comparison illuminations was identical to the target
illumination in every trial; the other comparison illumination was
selected at random from a lookup table containing the 12
comparison illuminations for that target illumination (the 0 DE
uv
comparison illumination was used twice, once for each of the two
6DE
uv
sets), resulting in each comparison illumination being
presented 10 times with the exception of each target illumination
which was presented at least twice in each trial and 20 times as a
comparison. The illumination presentation was time-locked to the
sound presentation, with a delay measured at less than 30 ms.
The box then remained dark and a final tone cued participants
to respond either 1 or 2 via the keypad. There was a minimum gap
of 1000 ms between trials which factored in the time taken for
participants to respond to the previous trial; trials were self-paced.
Each participant completed 480 trials per condition (2880 total,
240 per locus, with two targets per locus, and 10 per comparison).
Participants were given a mandatory 1 minute break after every
120 trials, but were also informed that they could break voluntarily
at any time and return, or withdraw.
Each experimental condition was conducted in a separate
session. Sessions were conducted at each participant’s convenience
and testing spanned a 6 weeks period.
Control experiment
One of the comparison illuminations for one of the targets on
one locus (the most extreme ‘‘red’’ comparison illumination, at
+58 dE) (10 trials per participant) was not shown correctly; instead
of the +58dE test illumination, the 0dE target was shown, due to a
miscommunication between the controlling computer and the
illuminator. Participants therefore performed not significantly
different from chance on these trials, as the comparison
illuminations were the same as the target. These 10 trials were
treated as 60DE from target trials and were removed from
analysis. To confirm the level of performance expected for this
illumination, the communication fault was corrected and a control
experiment conducted with 4 participants in which they
performed the task as before, with the correct comparison
illuminations, and using only the grey background condition with
no objects present (all illuminations were tested, not just that which
Colour Constancy by Illumination Discrimination
PLOS ONE | www.plosone.org 8 February 2014 | Volume 9 | Issue 2 | e87989

was not shown correctly). Accuracy for the extreme red
comparison illumination in this control experiment was not
significantly different from the 658 DE comparisons at the other
extremes, and not significantly different from 100%.
Cone contrast calculation
The modified MacLeod-Boynton (McB) coordinates of each
stimulus patch were computed as the scaled McB coordinates of
the stimulus relative (in contrast) to the McB coordinates of the
target illumination whitepoint. The McB coordinates r
McB
,b
McB
,
and lum
McB
are defined as l/(l+m),s/(l+m) and (l+m) respectively,
where l,mand sare the long-, middle- and short-wavelength cone
excitations of the stimulus. The contrast of the stimulus with
respect to the target whitepoint is calculated by the following
formulae (using the scaling factors of McDermott and Webster
[29]):
Red-green contrast: L-M~1953 rpatchMcB -rwhite McB

Blue-yellow contrast: S-LzMðÞ~5533 (bpatchMcB -bwhiteMcB )
Luminance contrast: LzM~(lumpatchMcB -lumwhite McB)=
lumwhiteMcB
The change in McB coordinates for each patch under the test
illumination relative to the target illumination was calculated by
subtracting the McB coordinate values under the target illumina-
tion from those under the test illumination (note that the
whitepoint for both sets of coordinates was held at that of the
target illumination).
Supporting Information
Figure S1 Histograms of changes in cone-opponent
channel excitations of 95 distinct background locations
between D67 and the bluer, redder, greener and
yellower illuminations ±18DE
uv
away in the grey
background condition, in modified MacLeod-Boynton
(McB) coordinates.
(TIFF)
Figure S2 Scene statistics from the grey background
condition. A. top: Surface reflectance function of the grey
background material (in blue) with .05 line marked (dashed line);
below: Plots of relative spectral power for each of the four extreme
metamer spectra: bluer, yellower, greener and redder, respectively.
B. Maximum, mean, skewness and kurtosis values for cone-
opponent contrast channel changes between D67 and the bluer,
redder, greener and yellower illuminations at each DE
uv
comparison in the grey background condition, in modified
MacLeod-Boynton (McB) coordinates.
(TIFF)
Figure S3 Chromaticity co-ordinates of 95 patches from
the Mondrian background condition (top) and grey
background condition (bottom) under D67 illumination.
The first principal components are marked with solid lines (slopes
of 0.93 and 21.08, respectively); blue lines indicate the blue-yellow
variation direction, and green lines the red-green variation
direction, respectively. The greatest variance occurs along the
blue-yellow direction in the Mondrian background, and along the
red-green direction for the grey background.
(TIFF)
Table S1 CIE 1931 xy chromaticity coordinates of
readings of the 34 illuminations used in the experiment,
for the two loci.
(DOCX)
Table S2 CIE 1931 xy chromaticity coordinates of the
fruit and matched printed papers used in the experi-
ment, measurements taken under D67.
(DOCX)
Author Contributions
Conceived and designed the experiments: BP AH. Performed the
experiments: BP SC. Analyzed the data: BP AH. Contributed reagents/
materials/analysis tools: MM GF BP. Wrote the paper: BP AH. Designed
software for hyperspectral imaging: SC.
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