TheJournalofExperimentalBiology
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© 2015. Published by The Company of Biologists Ltd | The Journal of Experimental Biology (2015) 218, 184-193 doi:10.1242/jeb.111187
ABSTRACT
Birds have impressive physiological adaptations for colour vision,
including tetrachromacy and coloured oil droplets, yet it is not clear
exactly how well birds can discriminate the reflecting object colours
that they encounter in nature. With behavioural experiments, we
determined colour discrimination thresholds of chickens in bright and
dim light. We performed the experiments with two colour series,
orange and green, covering two parts of chicken colour space. These
experiments allowed us to compare behavioural results with model
expectations and determine how different noise types limit colour
discrimination. At intensities ranging from bright light to those
corresponding to early dusk (250–10 cd m−2
), we describe thresholds
accurately by assuming a constant signal-to-noise ratio, in agreement
with an invariant Weber fraction of Weber’s law. Below this intensity,
signal-to-noise ratio decreases and Weber’s law is violated because
photon-shot noise limits colour discrimination. In very dim light (below
0.05cd m−2
for the orange series or 0.2 cd m−2
for the green series)
colour discrimination is possibly constrained by dark noise, and the
lowest intensity at which chickens can discriminate colours is 0.025
and 0.08 cd m−2
for the orange and green series, respectively. Our
results suggest that chickens use spatial pooling of cone outputs to
mitigate photon-shot noise. Surprisingly, we found no difference
between colour discrimination of chickens and humans tested with
the same test in bright light.
KEY WORDS: Animal behaviour, Bird vision, Gallus gallus,
Psychophysics, Visual modelling
INTRODUCTION
Birds use colour vision for many tasks, such as finding food and
choosing between mating partners (Bennett and Cuthill, 1994;
Cuthill et al., 1999). Bird colour vision is mediated by four single
cone photoreceptors sensitive to red light (long-wavelength
sensitive, LWS), green light (medium-wavelength sensitive, MWS),
blue light (short-wavelength sensitive, SWS) and violet or ultraviolet
light (very-short-wavelength sensitive, VS/UVS) (Osorio et al.,
1999; Hart, 2001). Bird cones are equipped with coloured oil
droplets that act as long-pass filters, which narrow spectral
sensitivity and reduce the overlap in sensitivity between cone types.
These retinal specializations are thought to improve colour
discrimination (Barlow, 1982; Govardovskii, 1983; Vorobyev, 2003)
and have inspired the hypothesis that bird colour discrimination
abilities should be superior to those of most other vertebrates,
including trichromatic humans (Vorobyev et al., 1998).
Discrimination of narrow-banded spectral lights has been studied
in some bird species (e.g. Maier, 1992; Prescott and Wathes, 1999;
RESEARCH ARTICLE
1
Lund University, Department of Biology, Sölvegatan 35, SE-226 32 Lund,
Sweden. 2
Lund University, Department of Philosophy, SE-221 00 Lund, Sweden.
3
The University of Auckland, Department of Optometry and Vision Science,
Auckland 1142, New Zealand.
*Author for correspondence (peter.olsson@biol.lu.se)
Received 16 July 2014; Accepted 4 November 2014
Goldsmith and Butler, 2003; Goldsmith and Butler, 2005; Lind et
al., 2014). These studies suggest that bird colour vision is
tetrachromatic, mediated by the single cones, which are compared
in opponent processing and well described as being limited by
receptor noise (Vorobyev and Osorio, 1998). However, most natural
colour stimuli are not narrow-banded spectral lights, but broadbanded
object reflectances, and the discrimination thresholds for
such object colours have never been tested in birds.
By absorbing light, the oil droplets not only narrow the spectral
sensitivity, but also reduce absolute sensitivity of cones, which could
affect colour discrimination in dim light (Vorobyev, 2003). The
intensity threshold for colour vision has been tested in only three
bird species, and they lose colour vision at higher intensities
compared with humans (Lind and Kelber, 2009a; Gomez et al.,
2014).
We assume that discrimination thresholds of visual systems are
set by noise (Vorobyev and Osorio, 1998; Lind and Kelber, 2009b),
and that noise can arise from different sources in different light
conditions. Over a wide range of relatively high light intensities, we
expect that Weber’s law holds, so that sensitivity changes
proportionally to light intensity and a constant Weber fraction (ω)
describes the signal-to-noise ratio that sets discrimination thresholds
(Donner et al., 1990; Osorio et al, 2004; Lind et al., 2014). Under
these conditions, the main source of noise is probably transducer
noise (Lillywhite and Laughlin, 1979; Howard and Snyder, 1983)
originating at the later stages of signal transduction in the
photoreceptors, possibly by fluctuations in cGMP levels (Angueyra
and Rieke, 2013). There are no electrophysiological measurements
of noise in bird photoreceptors, but rough estimates of noise have
been deduced from the results of behavioural experiments on
spectral sensitivity (Maier, 1992; Vorobyev et al., 1998; Lind et al.,
2014).
At lower intensities, photon-shot noise and dark noise gain
relative importance and lead to lower signal-to-noise ratios and thus
a violation of Weber’s law (Osorio et al., 2004). Photon-shot noise
is caused by the stochastic nature of photon arrival that follows
Poisson statistics. For a photon sample size N, the uncertainty is √N,
and the signal-to-noise ratio is N/√N, which is the de Vries–Rose law
(de Vries, 1943; Rose, 1942; Rose, 1948). It is proposed that visual
systems can use spatial pooling to reduce the effect of photon-shot
noise (e.g. Warrant, 1999). Pooling of signals from several
photoreceptors increases signal strength (N) and signal-to-noise
ratio, at the cost of spatial resolution. The absolute threshold of
vision is set by dark noise (X), which originates from spontaneous
activity in the photoreceptor that is indistinguishable from real
photon absorption (Barlow, 1956; Rieke and Baylor, 1998; Rieke
and Baylor, 2000). The signal-to-noise ratio of dark noise is
expressed as N/√X.
We used the chicken (Gallus gallus Linnaeus 1758) to test how
well birds can discriminate object colours in different light
intensities. For comparison, we tested human subjects under the
same conditions. We also compared the chickens’ performance with
predictions from an established colour vision model, the receptorBird
colour vision: behavioural thresholds reveal receptor noise
Peter Olsson1,
*, Olle Lind1,2,3
and Almut Kelber1
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RESEARCH ARTICLE The Journal of Experimental Biology (2014) doi:10.1242/jeb.111187
noise-limited (RNL) model (Vorobyev and Osorio, 1998). This
allowed us to address the following questions: (1) which Weber
fraction – and thus receptor noise level – explains chicken colour
discrimination thresholds in bright light best? (2) Over which
intensity range is Weber’s law valid for chicken colour
discrimination? (3) What is the intensity limit of chicken colour
vision? (4) Which types of noise are important for colour vision at
different light intensities? (5) Can birds discriminate smaller colour
differences than humans?
RESULTS
Colour discrimination in bright light
We trained newly hatched chicks to extract food crumbs from
coloured food containers and tested how well they could
discriminate a rewarded colour from unrewarded colours in two
alternative choice tests. We trained eight chicks to discriminate
between yellow and orange colours (orange series; Fig. 1A) and
another group of eight chicks to discriminate between blue and
green colours (green series; Fig. 1B). The unrewarded colours were
chosen such that the colour difference to the rewarded colour ranged
from well below 1 to above 3 just-noticeable differences (JNDs; see
supplementary material Table S1), according to the RNL model,
using a Weber fraction of 0.1 (Eqn 1 in the Materials and methods)
for the LWS mechanism that is commonly adopted for birds
(Vorobyev and Osorio, 1998; Lind et al., 2014). Colour differences
larger than 1 JND should be discriminable if correct parameters are
assumed and the model correctly describes behaviour (Vorobyev et
al., 2001; Lind et al., 2014).
The chicks first learned to discriminate the largest colour
differences (O5-O+ and G5-G+, Fig. 1) at 250 cd m−2
, and were then
tested with progressively smaller colour differences. They easily
discriminated all colour differences above 1 JND making between
80 and 100% correct choices. Discrimination performance dropped
rapidly for smaller colour differences, and the thresholds were found
to be 0.70±0.002 (mean ± s.d.) JNDs for the orange series and
0.58±0.03 JNDs for the green series (Fig. 1A,B).
We repeated the test at 10 cd m−2
and the colour discrimination
thresholds 0.69±0.06 JND and 0.56±0.05 JND for the orange and
green series, respectively, did not differ significantly from those at
250 cd m−2
(Fig. 1C,D). These results suggest that our initial
assumptions of noise levels were too high. The Weber fraction that
best explains the observed thresholds at both light intensities is 0.06,
for the LWS mechanism. This is the Weber fraction we included in
subsequent model calculations (Eqns 6 and 7).
List of symbols and abbreviations
d photoreceptor aperture diameter
D pupil diameter
f focal length of the eye
I illumination spectrum
JND just-noticeable difference
K absorption coefficient
l outer-segment length
L non-normalized receptor spectral sensitivity
LWS long-wavelength sensitive
MWS medium-wavelength sensitive
N absolute quantum catch
Q relative quantum catch
R normalised receptor spectral sensitivity
RNL receptor-noise limited
S reflectance spectrum
SWS short-wavelength sensitive
VS very-short-wavelength sensitive
X dark noise rate
Δf contrast in a receptor channel
Δt integration time
κ electrical conversion coefficient
λ wavelength
σ standard deviation of noise in a receptor
τ ocular media transmission
ω Weber fraction
0.5 1 1.5 2 2.5 3
0.4
0.6
0.8
1
0.5 1 1.5 2 2.5 3
0.4
0.6
0.8
1
0.5 1 1.5 2 2.5 3
0.4
0.6
0.8
1
Proportioncorrect
0.5 1 1.5 2 2.5 3
0.4
0.6
0.8
1
0.5 1 1.5 2 2.5 3
0.4
0.6
0.8
1
Colour difference (bird JND)
0.5 1 1.5 2 2.5 3
0.4
0.6
0.8
1
A B
C D
E F
4
3
2
1
No. subjects at
choice frequency
O+
O1
O3 O4 O5
O2
G+
G1
G2
G3 G4 G5
Fig. 1. Colour discrimination by chickens
and humans in bright light. (A–D) The
discrimination performance of chicks (n=8) for
the orange (left column) and green (right
column) series of colour stimuli. The x-axis
gives colour difference in the unit of JND, as
modelled assuming chicken colour vision and a
Weber fraction of 0.1 for the LWS channel (Eqn
1). Chicks tested at (A,B) 250 cd m−2
and (C,D)
10 cd m−2
. (E,F) Human discrimination
performance (n=4), plotted on the same scale
for colour differences as for the chicks. Each
filled circle refers to the fraction of correct
choices made for the last 40 choices for chicks
and 20 choices for humans at a given colour
difference. The size of the circle corresponds to
the number of subjects at a given choice level.
The lines are psychometric functions fitted to
the data of the group (see the Materials and
methods). The boxes and red lines refer to the
threshold. The inserts visualize the appearance
of the colour stimuli. Versions of the data with
fits to individual performance is available in
supplementary material Fig. S1.
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RESEARCH ARTICLE The Journal of Experimental Biology (2014) doi:10.1242/jeb.111187
For comparison, we asked two female and two male humans
with normal colour vision to participate in the same colour
discrimination experiments. Humans could discriminate the same
colours as chicks; their thresholds, 0.66±0.03 and 0.54±0.04
chicken JNDs for the orange and green series, respectively, were
similar to those of the birds (0.70±0.002 and 0.58±0.03)
(Fig. 1E,F). Human thresholds fit well with modelled (RNL model)
discriminability of the stimuli in human colour vision, using
published data on Weber fractions for each cone mechanism
(Wyszecki and Stiles, 2000).
Colour discrimination in dim light
We then tested chicken colour discrimination in even dimmer light,
at 3, 0.9, 0.1, 0.04 and 0.01 cd m−2
, to find the intensity threshold
for each colour pair that the chicks could discriminate in bright
light (Fig. 2). Performance generally remained high until it
dropped rapidly, close (within 1 log unit) to the intensity threshold.
In both series, the intensity threshold was lower for larger colour
differences between stimuli, but this effect declined dramatically
for the largest colour differences (Fig. 2C–H and Fig. 3A,B).
The intensity thresholds were consistently lower for the orange
than for the green series. The intensity threshold for discrimination
of the largest colour difference in the orange series was
0.025±0.008 cd m−2
and 0.080±0.027 cd m−2
for the green
series.
Human subjects were asked to discriminate O5 from O+ and G5
from G+ at several intensities; their intensity threshold was lower
than that of the chicks (see supplementary material Fig. S3). For
human subjects, just as for birds, the intensity threshold was lower
for the orange series than for the green series (see supplementary
material Fig S3).
Intensity thresholds, Weber’s law and absolute quantum
catch
Using the RNL model we found that Weber’s law and an invariant
Weber fraction (0.06 for the LWS mechanism) did not explain the
intensity thresholds for the discrimination of each colour pair at
light intensities below 10 cd m−2
. We had to assume higher noise
levels for the model to describe the behavioural thresholds
(Fig. 3A–D). We assumed that photon-shot noise and dark noise
caused the increase in noise level. The effects of photon-shot noise
and dark noise depend on absolute quantum catches (N) of
photoreceptors (Eqn 5), the calculation of which require
anatomical and physiological data. Using the information given in
Table 1 we determined the absolute quantum catches of individual
photoreceptors looking at the colour stimuli at the tested intensities
(Table 2; supplementary material Tables S2 and S3).
The effects of photon-shot noise and dark noise
Next, we modelled the effects of photon-shot noise and dark noise
using the data on absolute quantum catches. First, we considered the
addition of photon-shot noise (Eqn 6). Without spatial pooling,
photon-shot noise levels are too high to explain the observed
intensity thresholds (Fig. 3B,C). Spatial pooling improves signal-tonoise
ratio and thus colour discrimination in dim light (Fig. 4A,B).
We included spatial pooling in the model by multiplying absolute
quantum catches in each receptor channel with the relative amount
of spatial pooling (see Materials and methods for more information)
(Fig. 3C,D and Fig. 4A,B).
In line with expectations (Barlow, 1958), we found that we needed
to assume higher degrees of spatial pooling to explain the behavioural
results for all but the most contrasting colour pairs at dimmer light
intensities (Fig. 4A,B). In contrast, we found that to explain the
0.01 1 100
0.4
0.6
0.8
1
0.01 1 100
0.4
0.6
0.8
1
0.01 1 100
0.4
0.6
0.8
1
0.01 1 100
0.4
0.6
0.8
1
0.01 1 100
0.4
0.6
0.8
1
0.01 1 100
0.4
0.6
0.8
1
0.01 1 100
0.4
0.6
0.8
1
0.01 1 100
0.4
0.6
0.8
1
O3-O+ G3-G+
O4-O+
O5-O+ G5-G+
G4-G+
100.1
100.1
100.1
100.1
vs
vs
vs
vs
vs
vs
vs
vs
Intensity (cd m–2
)
Proportioncorrect
O2-O+ G2-G+
D
B
E
C
A
G
F
H
100.1
100.1
100.1
100.1
Fig. 2. Colour discrimination by chickens in
dim light. The discrimination performance of
chicks (n=4) for the four colour differences
discriminable in bright light (see Fig. 1) in
dimmer illumination for the orange series (left
column) and the green series (right column).
The boxes and red lines give threshold values
at 65% correct choices, the circles refer to the
level of performance of the chicks, and circle
size corresponds the number of individuals at
that level. The lines are psychometric functions
fitted to the data of the group. Versions of the
data with fits to individual performance are
available in supplementary material Fig. S2.
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RESEARCH ARTICLE The Journal of Experimental Biology (2014) doi:10.1242/jeb.111187
discrimination of the largest colours differences in each series (O5-O+
and G5-G+) we had to assume considerably less spatial pooling than
for all other colour differences at higher light intensities. To explain
this inconsistency, we also considered the effects of dark noise.
We adopted two approaches of taking the effects of dark noise
into account. The first approach assumes linear signal transfer from
the photoreceptors, meaning that there is equal pooling of false dark
events and true photon catches, and resulting in one Weber fraction
for each level of pooling (Eqn 7). Because dark noise levels are
unknown for bird cone photoreceptors, we modelled dark noise
levels between 10 and 600 events per second (Fig. 4C,D). The larger
the rate of dark noise, the more spatial pooling was required to
model the behavioural threshold (Fig. 4C,D). However, this
approach again indicated that less spatial pooling should be required
to explain the intensity thresholds of the largest colour differences
(O5-O+ and G5-G+; supplementary material Fig. S6).
Therefore, in a second approach, we assumed nonlinear signal
transfer from the photoreceptors (Field and Rieke, 2002). This way,
dark noise sets a threshold quantum catch, in individual
photoreceptors, that a light signal must surpass in order to be
conveyed. To estimate this threshold, we plotted the absolute
quantum catches of individual photoreceptors from the unrewarded
colours at various intensity levels (see supplementary material
Fig. S4). We estimated the dark noise threshold by the absolute
quantum catches of individual photoreceptors from the stimuli at the
dimmest light intensities. In the orange series, the MWS
photoreceptor sees the largest difference in quantum catch between
the rewarded and unrewarded stimuli; in the green series, it is the
VS photoreceptor. Absolute quantum catch of a single MWS cone
is ca. 7 and 9 per second from O+ and O4, respectively, at the
intensity threshold of O4-O+, and 6 and 9 per second from O+ and
O5 respectively, at the intensity threshold of O5-O+. Absolute
quantum catch of a single VS cone is ca. 4 and 5 per second from
G+ and G4, respectively, at the intensity threshold and 3 and 5 per
second from G+ and G5, respectively, at the intensity threshold of
G5-G+ (Fig. 4E,F; Table 2 and see supplementary material Tables
S2 and S3). If we assume that a signal-to-noise ratio of 1 sets the
threshold, and follow the calculations from Donner (Donner, 1989)
(Eqn 8), our results suggest that a chicken MWS cone has a dark
noise rate in the range of ~30–70 events per second and a VS cone
has a dark noise rate of ~6–30 events per second.
DISCUSSION
Colour discrimination thresholds in bright light and the
Weber fraction
Our results allow us, for the first time, to compare the predictions of
the RNL model to behavioural discrimination thresholds for object
0
2
4
6
0.01 1 100
Colourdifference
(JND)
0
2
4
6
0.01 1 100
0.01 1 100
0
0.2
0.4
0.6
0.8
Weberfraction
(M/LWS)
1 100
0
0.2
0.4
0.6
0.8
Intensity (cd m–2
)
A
C
B
D
Noise with no spatial pooling
Noise with spatial pooling
Noise to reach threshold
0.01
100.1
100.1
O5-O+
O4-O+
O3-O+ O2-O+
G5-G+
G4-G+
G3-G+
G-G+2
0.1 10
100.1
Fig. 3. Intensity thresholds and noise.
(A,B) Mean (±s.d.) intensity thresholds for the
colour differences in the orange and green
series, respectively. Colour differences are
modelled assuming a 0.06 Weber fraction for
the LWS channel. (C,D) Weber fraction for the
LWS channel required to consolidate the
modelled colour difference of a given colour
task with their respective intensity threshold
(solid lines). The Weber fraction obtained
assuming the addition of photon shot noise
(Eqn 6) without spatial pooling (long dashed
lines, assuming an integrative field containing 1
VS, 2 SWS, 4 MWS, 4 LWS cones), or with
spatial pooling in a 25-fold integrative field
(short dashed lines, 25 VS, 50 SWS, 100 MWS,
100 LWS) between the rewarded colour and
each unrewarded colour in the orange and
green series, respectively.
Table 1. Values of parameters used for modelling absolute
quantum catches
Parameter (unit) Value Ref.
Cone outer segment length (μm) 30 This study
Absorption coefficient 0.035 Bowmaker et al., 1977
Ellipsoid diameter (μm) 3.1 This study
Focal length (μm) 8300 This study
Pupil diameter (max–min) (μm) 4900–3500 This study
F-number (min) 1.66 This study
Transmission of ocular media τ (%) 80 Johnsen, 2012
Quantum transduction efficiency κ (%) 50 Johnsen, 2012
Integration time (max–min) (ms) 50–12 Lisney et al., 2011
Max. optical sensitivity Sw (μm2
sr) 0.67 This study
Table 2. Quantum catches of single receptor types per integration
time
Quantum catch (photons)
Intensity (cdm−2
) Stimulus LWS MWS SWS VS
250 O+ 1234 492 349 169
10 O+ 180 72 51 25
0.9 O+ 24 9.2 2.4 1.5
0.1 O+ 2.7 1.1 0.6 0.2
0.04 O+ 1.1 0.4 0.2 0.07
0.01 O+ 0.3 0.1 0.07 0.02
250 G+ 447 537 476 213
10 G+ 65 78 70 31
0.9 G+ 7.1 8.1 2.8 1.8
0.1 G+ 0.8 0.9 0.7 0.2
0.04 G+ 0.3 0.4 0.3 0.08
0.01 G+ 0.09 0.1 0.08 0.02
Values are calculated from the rewarded colour stimulus in the orange and
green series in the different intensity levels using Eqn 5 and values from
Table 1. Excerpt of full tables available in supplementary material Tables S2
and S3. D=0.350, 0.415, 0.466, 0.472, 0.477 and 0.499cm; Δt=0.012, 0.025,
0.05, 0.05, 0.05 and 0.05s for the intensities, 250, 10, 0.9, 0.1, 0.04 and
0.01cdm−2
, respectively.
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RESEARCH ARTICLE The Journal of Experimental Biology (2014) doi:10.1242/jeb.111187
colours in bright and dim light in birds. Initially, we assumed a noise
level corresponding to a Weber fraction of 0.1 for the LWS channel,
as suggested from behavioural tests of spectral sensitivity in the pekin
robin (Leiothrix lutea) (Maier and Bowmaker, 1993; Vorobyev and
Osorio, 1998) and the budgerigar (Melopsittacus undulatus) (Lind et
al., 2014). To describe the chicks’ performance in bright light, in 250
and 10 cd m−2
, and to set the observed discrimination threshold to
1 JND, we have to assume a lower Weber fraction of 0.06.
If chickens truly have lower receptor noise levels than
budgerigars, this could also help to explain their higher contrast
sensitivity (7% Michelson contrast) (Schmid and Wildsoet, 1998;
Jarvis et al., 2009), compared with budgerigars (11% Michelson
contrast ) (Lind and Kelber, 2011). Another explanation could be
that exploiting the natural chicken behaviour – pecking at objects on
the ground to get food – may facilitate better performance than using
the artificial task of associating food with spectral lights as with the
pekin robins and budgerigars (Maier and Bowmaker, 1993; Lind et
al., 2014).
Intensity range of Weber’s law
In a recent study on budgerigars, behavioural spectral sensitivity
data could be explained with a constant Weber fraction over a wide
range of background intensities above 1 cd m−2
, whereas other
noise sources had to be taken into account at lower intensities
(Lind et al., 2014). Our current study of object colour
discrimination in chickens shows that an invariant Weber fraction
describes thresholds at both 250 and 10 cd m−2
, whereas noise
increases at lower intensities (Fig. 3C,D). Similar to these results,
colour discrimination thresholds in humans increase strongly at
intensities below ~3 cd m−2
compared with brighter light intensities
(Brown, 1951; Yebra et al., 2000).
The absolute intensity limit of colour discrimination
We used the intensity thresholds for the orange and green stimulus
pairs O5-O+ and G5-G+ (colour differences are 5.3 and 5.4 JNDs,
respectively, with a Weber fraction of 0.06 for the LWS mechanism;
Fig. 2G,H; Fig. 3A,B) as estimates of the absolute intensity limit of
colour discrimination. We found that the intensity threshold in the
green series, 0.08 cd m−2
, of chickens was comparable to thresholds
for blue–green colour discrimination of budgerigars (Melopsittacus
undulatus), Bourke’s parrots (Neosephotus bourkii) and blue tits
(Cyanistes caeruleus), which could discriminate colours at
0.1 cd m−2
, 0.4 cd m−2
and between 0.05 and 0.2 cd m−2
, respectively
(Lind and Kelber, 2009; Gomez et al., 2014). Our results show that
the intensity threshold for colour discrimination depends on colour
difference between the stimuli, but this dependence is reduced for
colour differences larger than about 3 JND (Fig. 3A,B). In the blue
tit study, the colour differences are very large, in the budgerigar and
Bourke’s parrot study colour differences are slightly smaller but
above 4 JND (O.L., unpublished data) and thus also reflect absolute
intensity thresholds. The intensity threshold for the orange series
was lower, 0.025 cd m−2
, this is probably partly an effect of the
orange series being generally brighter than the green series [ca. 1.5times
brighter for chicken double cones (O+/G+)]. Blue and green
colours have band-pass characteristics and are therefore generally
dimmer than orange and yellow colours (see supplementary material
Fig. S5).
Colour discrimination in dim light: which sources of noise
limit colour vision in dim light?
Photon-shot noise
Without spatial pooling, photon-shot noise was too large to explain
the intensity thresholds we observed (Fig. 3C,D; Fig. 4A,B), even
0 50 100
0
1
2
3
0 50 100
0
1
2
3
0 50 100
0
1
2
3
0 50 100
0
1
2
3
0.01 0.1 1
0
10
20
Quantumcatch(es)MWS
0.01 0.1 1
0
10
20O5
O4
O3
Quantum catch
threshold
10 events s–1
100 events s–1
600 events s–1
O5
O4
O3
O2
G5
G4
G3
G2
G5
G4
G3
Quantum catch
threshold
A B
D
FE
C
Spatial pooling (no. integrative fields)
Intensity (cd m–2
)
VS
Modelledcolourdifference(JND)
Fig. 4. The effect of photon-shot noise and dark noise on
intensity thresholds of chicken colour vision. (A–D) The
modelled colour difference between rewarded and
unrewarded colours at their intensity thresholds assuming
different levels of spatial pooling. The model predictions
agree with the behavioural data when the modelled colour
difference reaches 1 JND. An integrative field contains 1 VS,
2 SWS, 4 MWS and 4 LWS cones, a twofold integrative field
contains 2 VS, 4 SWS, 8 MWS and 8 LWS cones, etc.
(A,B) Weber fractions are set by photon-shot noise (Eqn 6)
combined with spatial pooling. (A) The colours in the orange
series O5, O4, O3 and O2 modelled at 0.025, 0.033, 0.09
and 10 cd m−2
, respectively. The degree of spatial pooling
required to reach threshold (1 JND) is 2, 20, 20 and 6-fold
integrative fields for O2, O3, O4 and O5. (B) The colours in
the green series, G5, G4, G3 and G2 modelled at 0.08, 0.1,
0.26 and 10 cd m−2
. The degree of spatial pooling required to
reach threshold (1 JND) is 11, 24, 26 and sixfold integrative
fields for G2, G3, G4 and G5. (C,D) Weber fractions are set
by photon-shot noise and dark noise (Eqn 7) combined with
spatial pooling. (C) O5 at 0.025 cd m−2
, at four different levels
of dark noise. (D) G5 modelled at 0.08 cd m−2
, at four
different levels of dark noise. (E) The absolute quantum
catches (per second) of a single MWS cone from O5, O4 and
O3 at various intensities. (F) The absolute quantum catches
of a single VS cone from G5, G4 and G3 at various
intensities. The red lines in E and F show proposed quantum
catch thresholds.
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RESEARCH ARTICLE The Journal of Experimental Biology (2014) doi:10.1242/jeb.111187
at the intensity of 10 cd m−2
, where the colour discrimination
threshold was similar to that in bright light (250 cd m−2
). In our
model we needed to assume less spatial pooling (between two- and
11-fold integrative fields) to explain discrimination of the colour
pairs O2-O+ and G2-G+, at 10 cd m−2
and more spatial pooling (20to
25-fold integrative fields) to explain discrimination of O3-O+,
G3-G+, O4-O+ and G4-G+ at dimmer light intensities. This fits the
expectation that spatial pooling increases in lower light intensities
(Barlow, 1958). However, the discrimination of the colour pairs O5O+
and G5-G+ (with the largest differences from the respective
rewarded colour) could be explained assuming considerably less
spatial pooling (about six-fold integrative fields), which was
puzzling and inconsistent with our expectations. We conclude that
most likely, this inconsistency indicates that our model of dim light
colour discrimination is too simplified, because it does not account
for yet another source of noise, dark noise (Barlow, 1956).
Dark noise
Dark noise sets the ultimate intensity limit of vision (Barlow, 1956;
Donner, 1992) but how it affects colour vision is unclear. The level
of dark noise, defined by the rate of spontaneous photon-absorptionlike
events (dark events) in a single receptor, has been well
described in rods and is usually in the range of 0.01 dark events per
second (Rieke and Baylor, 1998). In cones, however, good estimates
of dark noise are difficult to obtain, and range from fewer than 10
up to several thousands of events per second (Lamb and Simon,
1977; Schnapf et al., 1990; Donner, 1992; Schneeweis and Schnapf,
1995; Rieke and Baylor, 2000; Fu et al., 2008; Angueyra and Rieke,
2013). We could find no satisfactory fit to our behavioural data
assuming linear signal transfer from the photoreceptors, despite
modelling a large range of dark noise rates (see supplementary
material Fig. S6).
Nonlinear signal transfer has been found in the rod–rod bipolar
cell synapse (Field and Rieke, 2002), which provides a threshold in
the synapse between the photoreceptor and the bipolar cell. This
threshold rejects weak signals from the photoreceptor, thereby
effectively removing false signal from dark events but also some
real signal from captured photons. This apparent loss is counteracted
by the benefit that only signal from real photon catch is transmitted
to the bipolar cell. It is unknown whether this nonlinearity is also
present in the cone pathway, or whether it is present at all in birds.
However, the existence of such thresholds could help to explain our
data. The sharp cut-off in the decrease of intensity threshold despite
increasing colour difference (Fig. 3A,B) conforms to the idea that a
threshold exists at the photoreceptor level. The intensity threshold
of the two largest colour differences in each series may be set when
they reach such a threshold in quantum catch and we estimate this
threshold to be ~5–10 photons per second (red lines in Fig. 4E,F).
Although our estimates of dark noise rate are coarse, they are in line
with the idea that the λmax of the photoreceptor is important for its
dark noise rate (Barlow, 1957; Firsov and Govardovskii, 1989;
Rieke and Baylor, 2000; Ala-Laurila et al., 2004).
Can birds discriminate smaller colour differences than
humans?
Chickens and humans performed equally well in our colour
discrimination experiments. It seems that colour discrimination
performance within the human visual range (about 400–700 nm) is
similar in humans and birds, an idea also suggested by Lind et al.
(Lind et al., 2014). The Weber fraction for human LWS cone
photoreceptors has been suggested to be 0.018 (Wyszecki and Stiles,
2000), which is much smaller than the Weber fraction for bird LWS
cones (0.06 for chickens and 0.1 for Leiothrix and budgerigars).
This difference could explain why we found no obvious difference
in colour discrimination threshold between humans and birds and
may also help to explain why birds generally have lower contrast
sensitivity than humans (Ghim and Hodos, 2006; Gover et al., 2009;
Harmening et al., 2009; Lind et al., 2012).
Concluding remarks
The RNL model describes thresholds of colour discrimination
accurately over a wide range of intensities, using realistic
physiological assumptions. (1) For the chicken, Weber’s law holds
at least for intensities down to 10 cd m−2
with a Weber fraction of
0.06 for the LWS channel. (2) At lower light intensities photon-shot
noise affects colour discrimination. To counteract the effect of
photon-shot noise, the retina of birds must use spatial and temporal
pooling. At very low light intensities, dark noise explains the
intensity threshold of colour discrimination, by determining the
minimum quantum catch for making single photoreceptors useful
for vision. (3) The intensity threshold of colour discrimination
depends on the amplitude of stimulus reflectance (brightness) and
the difference in spectral composition between the stimuli; bright
stimuli with large colour difference can be discriminated in lower
light intensities. (4) Assuming that intensity thresholds of colour
discrimination are set by photon-shot noise alone can overestimate
the intensity threshold of large colour differences. (5) Birds and
humans did not differ significantly in how they discriminated small
colour differences, however humans could discriminate colours at
lower light intensities than birds. Birds should still be able to see
colours that humans cannot discriminate because their visual range
extends into the ultraviolet region of the spectrum. Taken together,
our results add understanding of the process underlying colour
discrimination thresholds, and allow for more realistic and reliable
modelling of colour discrimination in natural contexts, both in bright
and dim light.
MATERIALS AND METHODS
Animals
Chicks of the Lohman White breed were obtained as eggs from a local
breeder (Gimranäs AB, Herrljunga, Sweden), hatched in a commercial
incubator (Covatutto 24, Högberga AB, Matfors, Sweden) and housed in
12 h:12 h light:dark cycle. Animals were kept in accordance with regulations
specified by the Swedish board of Agriculture and approved for housing by
the same agency (Reg. no. 6.2.18-8245/13). Experiments were approved by
the responsible ethical committee (Reg. no. 6-12). Both male and female
chicks were used in the experiments. During training and testing, the birds
were fed only during the experiment; at the end of the day, they were
allowed a full feeder that was removed on the following morning at least 2 h
before experiment. The birds were about 3 weeks old when they finished the
bright light experiment and about 5 weeks old when they finished the dim
light experiment.
Illumination
The experimental cage (70×40 cm wooden box) was illuminated from
above by fluorescent tubes (Biolux L18W/965; Osram, München,
Germany) at 250 cd m−2
and 10 cd m−2
and white LEDs (white 5 mm, 20 cd;
Kjell & Company, Malmö, Sweden) at all lower light intensities (Fig. 5).
The intensity of the illumination was measured from a white paper on the
floor of the cage using a radiometer (ILT1700 Research Radiometer,
International Light, Peabody, MA, USA). The illumination radiance
spectrum was measured from a white standard on the floor of the
experimental cage with a calibrated spectroradiometer (RSP900-R;
International Light, Peabody, MA, USA). The radiance spectrum at
intensities below 3 cd m−2
was determined by scaling the radiance
measurements according to the radiometric measurements. We used seven
TheJournalofExperimentalBiology
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RESEARCH ARTICLE The Journal of Experimental Biology (2014) doi:10.1242/jeb.111187
intensity levels: 250 cd m−2
, 10 cd m−2
, 3 cd m−2
, 0.9 cd m−2
, 0.11 cd m−2
,
0.04 cd m−2
and 0.01 cd m−2
.
Stimuli
The stimuli were adapted from a previous study (Osorio et al., 1999).
Colours were created in Adobe Illustrator, Creative Suite package version
5, using CMYK colour coding. We created rectangles measuring 30×35 mm
that were subdivided into 90 small rectangles measuring 2×6 mm. A random
subset (30%) of the small rectangles were coloured and the remaining 70%
had shades of grey of random (0.3 Michelson contrast range for the double
cone) intensity. These patterns were printed on paper and folded into conical
food containers. Two series of colours were created to test two parts of
chicken colour space and variation for different receptor types (Table 1;
Fig. 5C). In order to find behavioural thresholds, the colours in each series
were chosen to lie in a line in the tetrachromatic colour space, thus
generating a range of colour differences between the rewarded and the
unrewarded colours. The black ink value of the colour rectangles was varied
randomly to yield intensity variation over a 0.05−0.17 Michelson contrast
range to make intensity an unreliable cue. The maximum contrast between
an average intensity colour stimulus and the average intensity of the grey
fields was 0.09 Michelson contrast for the double cone. The brightness
contrast between the colours (the brightest version of each stimulus) within
each series was lower than 10% (Michelson contrast) for the chicken double
cones, thus below their brightness discrimination threshold (Jones and
Osorio, 2004), in all conditions.
In bright light the radiance spectrum (R and I in Eqn 2 are already combined
in the measurement) of the colours was measured directly from a paper with
printed colour. However, for the dim light experiment the spectroradiometer
was not sensitive enough to measure the colours directly. Therefore the
reflectance (see supplementary material Fig. S5) of the colours was measured
under a broad-spectrum Xenon light source (HPX-2000-HP-DUV, Ocean
Optics, Dunedin, FL, USA), with a spectrometer (Maya 2000, Ocean Optics,
Dunedin, FL, USA) and combined with the radiance spectrum of the
illumination to reconstruct the radiance spectra of the colours in dim light.
Visual modelling of performance in bright light
Colour differences were calculated using the RNL model proposed by
Vorobyev and Osorio (Vorobyev and Osorio, 1998). Photoreceptor
sensitivities (Fig. 5B) were modelled by combining known peak absorbance
of the chicken cone visual pigments with the transmittance data for oil
droplets and ocular media (Bowmaker et al., 1997; Lind and Kelber, 2009a)
using the template by Govardovskii et al. (Govardovskii et al., 2000).
Human visual pigments were modelled with the Govardovskii template,
using the absorbance peaks of human visual pigments (Dartnall et al., 1983)
including the filtering by human ocular media and macular pigment
(Wyzecki and Styles, 2000) for the LWS and MWS cone (see supplementary
material Fig. S7).
The RNL model assumes that the discrimination thresholds are set by
photoreceptor noise, which is propagated into higher order processing. We
assumed that spatial pooling can be used to improve signal-to-noise ratio so
that a limiting Weber fraction for a receptor channel i can be calculated as:
where ω is the Weber fraction, σ is the standard deviation of the noise in a
single cone channel and η is the relative abundance of cone type i. The
relative abundance of each cone type in chicken is 1:2:4:4 for the VS, SWS,
MWS and LWS type, respectively (Kram et al., 2010). We initially assumed
a standard deviation of noise of 0.2, which leads to a Weber fraction of 0.1
for the LWS and MWS channels as suggested for other birds, by Vorobyev
and Osorio (Vorobyev and Osorio, 1998) and Lind et al. (Lind et al., 2014).
However, for modelling discrimination in dim light we used the Weber
fraction that resulted from our behavioural tests (Fig. 1).
The quantum catch Q of cone type i is given by:
where R is the spectral sensitivity of photoreceptor i (i = VS, SWS, MWS,
LWS), S is the reflectance spectrum of the stimulus and I is the radiance
spectrum of the illumination.
To model the discriminability of two stimuli, we then calculated the
difference (Δfi) in quantum catch between the stimuli for each cone type as:
We assumed that the photoreceptors are appropriately adapted to the
background for optimal performance.
The discriminability of two colour stimuli for a tetrachromatic animal, such
as the chicken, was calculated as:
where ΔS is the colour difference (sometimes distance/chromatic contrast)
in the unit of just noticeable differences (JND), where differences ≥1 JND
are assumed to be discriminable (Vorobyev et al., 2001; Lind et al., 2014) .
f
Q
Q
ln
stimulus 1
stimulus 2
. (3)i
i
i
Δ =
⎛
⎝⎜
⎞
⎠⎟
, (1)i
i
i
ω =
σ
η
Q R S I , (2)i i
300
700
∫ ( ) ( ) ( )= λ λ λ Δλ
S
f f f f
f f f f
f f f f
( )
, (4)2
2
4 3
2 2
4 2
2
2
3 2
2 2
4 1
2
2
3 1
2 2
2 1
2
2 2 2 2
1 2 1 3
1 4 2 3
2 4 3 4
1 2 3 1 2 4 1 3 4 2 3 4
( ) ( ) ( ) ( )
( ) ( ) ( )
( ) ( ) ( ) ( )
( ) ( ) ( ) ( )
Δ =
ω ω Δ − Δ + ω ω Δ − Δ
+ ω ω Δ − Δ + ω ω Δ − Δ
+ ω ω Δ − Δ + ω ω Δ − Δ
ω ω ω + ω ω ω + ω ω ω + ω ω ω
300 400 500 600 700
0
0.2
0.4
0.6
0.8
1
Wavelength (nm)
Normalisedradiance
300 400 500 600 700
0
0.2
0.4
0.6
0.8
1
Sensitivity
LWS
SWS1
MWS
SWS2
A B CVS SWS MWS LWS
Fluorescent tubes
LED
Greenseries
Orange series
Fig. 5. Illumination spectra, chicken cone sensitivity and chromaticity diagram. (A) The radiance spectrum of the fluorescent tubes (solid line) and the
LEDs (dashed line) at 250 and 3 cd m−2
respectively. The maxima of the normalised spectra refer to 1.5×1011
photons cm2
s−1
sr−1
(fluorescent tubes) and
1.5×109
photons cm2
s−1
sr−1
(LEDs). (B) The spectral sensitivity of the four single cones in the chicken retina, accounting for oil droplet and ocular media
filtering. The solid lines represent the normalized sensitivity used to determine relative quantum catch in bright light, the dashed lines represent the lower
sensitivity resulting from oil droplet and ocular media filtering, used to calculate absolute quantum catch in dim light. (C) The loci of the two colour series in a
chicken chromaticity diagram, assuming no adaptive background. The black circles refer to the rewarded colour in each series, the orange on the right side and
the green on the left. The open circle represents the achromatic point.
TheJournalofExperimentalBiology
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RESEARCH ARTICLE The Journal of Experimental Biology (2014) doi:10.1242/jeb.111187
Modelling colour differences in dim light, absolute quantum
catch
To estimate photon-shot noise and dark noise we determined absolute
quantum catch, N, for each receptor type, using the following equation:
where d is the diameter of the ellipsoid in the inner segment, f is the focal
length of the eye, D is the pupil diameter, κ is the electrical conversion
coefficient, which is set to 50%, τ is the absolute transmission of the ocular
media independent of wavelength, which is set to 80%, Δt is the integration
time of the photoreceptor (determined from the flicker fusion frequency
measured by Lisney, 2011), A is cone sensitivity considering the reduction
in quantum catch from the filtering of oil droplets and ocular media (dashed
curve in Fig 5), k is the absorption coefficient, l is the length of the outer
segment in μm, and L is the radiance of the stimulus (Johnsen, 2009). The
values of f, D, d and l were determined as described below. The values we
used are given in Table 2. The intensity-dependent radiance measurements
L can be obtained from P.O. on request.
Photon-shot noise
We included photon-shot noise into the RNL model by using Equations 2,
3, and 4 but substituting the Weber fraction with the noise term from this
equation:
where N is the average of the absolute quantum catches of the two stimuli
compared and ω is the Weber fraction calculated from Eqn 1 of a receptor
type i, estimated from our behavioural results (Fig. 1). We assumed that the
retina of the bird can use spatial pooling to increase the absolute quantum
catch N, and thereby the signal-to-noise ratio incurred by photon-shot noise.
We assumed that the level of spatial pooling that can occur within a channel
is determined by its relative abundance in the retina, similar to Vorobyev and
Osorio (Vorobyev and Osorio, 1998). One set of single cones contains
1:2:4:4 VS, SWS, MWS and LWS cones, respectively, which represents
relative cone abundance and without spatial pooling, this correlates to one
integrative field. A twofold integrative field contains 2:4:8:8 VS, SWS,
MWS and LWS cones, respectively, and so forth. The absolute quantum
catch N of a given cone channel is then multiplied by the corresponding
number of receptors of that type within the total integrative field.
Dark noise
We included the effect of dark noise to the RNL model by calculating a new
Weber fraction as:
where X is the number of photon-equivalent dark events in the receptor
channel i, and N is the absolute quantum catch and ω is the Weber fraction
from Eqn 1 of receptor channel i, estimated from our behavioural results
(Fig. 1). The effective X for a given channel was determined by
multiplying the dark noise rate with the integration time of the
photoreceptors and the number of cones of that type in the integrative
field. To model colour difference including the effect of dark noise we
used Eqns 2, 3 and 4 but substituted the Weber fraction by the noise term
from Eqn 7. We assumed the same rate of dark noise for all receptor types
and evaluated several rates of dark noise, 10, 100 and 600 dark events per
second per cone.
To calculate the dark noise rate in a single receptor we used the equation
from Donner (Donner, 1989):
where SNR is the signal-noise ratio, N is the quantum catch and X is the
dark noise. We assumed that an SNR of 1 sets the threshold and then
calculated the dark noise rate.
N N X
N
, (7)i
i i i i
i
,dark
2 2
ω =
ω + +
N
d
f
D t e L
4
1 , (5)i
kA l
2 2
2
300
700
∫ ( ) ( )=
π⎛
⎝⎜
⎞
⎠⎟
⎛
⎝⎜
⎞
⎠⎟ κτΔ − λ Δλ( )− λ
N N
N
, (6)i
i i i
i
,photon
2 2
ω =
ω +
N
N X
SNR , (8)=
+
Anatomical data and pupil dynamics
The pupil diameters of three chickens were measured at ca. 5 weeks of age,
at the intensities used in the study (see supplementary material Fig. S8). We
attached a white spherical plastic marble to the head of the animals as a
reference and filmed them with a video camera (Sony HDR-XR 500VE) in
night-shot mode. At each intensity level, the largest pupil diameter D
measured from each individual bird was used. The focal length f was
determined from frozen sections of chicken eyes (ca. 5 weeks old) after the
behavioural experiment, as previously reported in Lind and Kelber (Lind
and Kelber, 2009b). The photoreceptor ellipsoid diameter d and outer
segment length l were measured in transmission electron micrographs of
some animals used in the behavioural studies at ca. 5 weeks of age.
Behavioural assays
Initial training of stimulus association
Forty-eight hours after hatching, the chicks were trained, in groups of 4 to
5 individuals, to associate food containers of the rewarded colour with food.
The folded container contained a small portion of commercial chick crumbs
that spilled out when the chick pecked at the container. On the second day
the chicks were trained in pairs with the same tasks. The third day they were
trained, in pairs, to walk out into the arena after a partition wall was
removed to get access to a single food container. On the fourth day the same
procedure was repeated but chicks were trained one by one.
Colour discrimination in bright light
From the fifth day onwards each chick was presented with a rewarded food
container and an unrewarded food container of a different colour, for the first
time. After the partition wall was removed, the chick was allowed to make
one peck at one container. If the chick pecked at the container of the
rewarded colour the food spilled out and the container with the unrewarded
colour was removed. If it pecked the container of the unrewarded colour
both containers were removed. The unrewarded colour that the chicks
encountered first was the colour most different (O5-O+, or G5/G+, for the
green series) from the rewarded colour. The two stimuli were separated by
5 cm. Each chick (n=8 for each series) had two training sessions with 20
trials per day, one in the morning and one in the afternoon. After a chick had
reached 75% correct choices for two consecutive sessions indicating that it
had learned the task, we continued with the unrewarding colour of the next
smaller colour difference in the respective series. For all but one chick it
took four sessions to reach our criterion; one chicken took five sessions to
reach learning criterion. For each colour difference the animals had four
sessions, data from the last two sessions were analysed.
Colour discrimination in dim light
The dim-light experiment started after the bright-light experiment was
finished. In each series, the chicks were divided in two groups, four of them
continued first with O5 (G5, for the green series) and then O3 (G3) as
unrewarding colours in increasingly dim light intensities, the other four with
O4 (G4) and then O2 (G2). They were first trained again in bright light, to
a given colour difference for two sessions, the next day in the next lower
intensity, and each following day the intensity was lowered further. Each
animal had two sessions consisting of 20 presentations, at every intensity
level. Prior to the experiment the birds were adapted to the intensity. We
allowed different adaptation times according to the intensity level, 5, 7, 10,
15 and 15 min to 10, 0.9, 0.1, 0.4 and 0.01 cd m−2
, respectively.
Tests with human subjects
Four subjects (25–35 years old) with stated normal colour vision agreed to
participate in a comparative experiment with the exact same stimuli and
illumination conditions as with the chickens. Subjects were allowed 2 min
to familiarize with the rewarded stimulus and then tested in a two-choice
experiment starting in bright light and the largest colour difference (O/G5O/G+).
No positive feedback was given. Humans were allowed the same
adaptation times as the birds for each intensity level.
Data analysis
For each animal, the percentage of correct choices of the last 40 choices for
each task was plotted against the calculated colour difference or intensity.
TheJournalofExperimentalBiology
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RESEARCH ARTICLE The Journal of Experimental Biology (2014) doi:10.1242/jeb.111187
We fitted a logistic function to the data using the Palamedes toolbox in
Matlab (Prins and Kingdom, 2009). The fitted lines in Figs 1 and 2 are
logistic functions fitted to the group data, and the thresholds reported in the
text are means ± s.d. of thresholds derived via bootstrapping of the fitted
function. We used a binomial test to decide on a threshold for discrimination
at 65% correct choices (N=40, P<0.05, one-tailed binomial test) for chickens
and a threshold of 75% for humans (N=20, P<0.05, one-tailed binomial test).
We then altered the noise variable in the RNL model to consolidate the
modelled threshold and the behavioural threshold, such that 1 JND coincides
with the behavioural threshold.
Acknowledgements
We thank Christine Scholtyßek and two anonymous reviewers for helpful
comments on earlier versions of the manuscript. We also thank our colleagues in
the Vision Group at Lund University and our collaborators Joseph Corbo and
Matthew Toomey from Washington University in St Louis and Nicholas Roberts
and David Wilby from Bristol University for fruitful discussions.
Competing interests
The authors declare no competing financial interests.
Author contributions
A.K., O.L. and P.O. conceptualised the study. P.O. performed the experiments.
A.K., O.L. and P.O. analysed and interpreted the data. P.O. wrote the manuscript
with contributions from A.K. and O.L.
Funding
The work was funded by the Human Frontier Science Programme
[RGP0017/2011], the Swedish Research Council [621-2009-5683, 2012-2212 and
637-2013-388] and the K A Wallenberg Foundation.
Supplementary material
Supplementary material available online at
http://jeb.biologists.org/lookup/suppl/doi:10.1242/jeb.111187/-/DC1
References
Ala-Laurila, P., Donner, K. and Koskelainen, A. (2004). Thermal activation and
photoactivation of visual pigments. Biophys. J. 86, 3653-3662.
Angueyra, J. M. and Rieke, F. (2013). Origin and effect of phototransduction noise in
primate cone photoreceptors. Nat. Neurosci. 16, 1692-1700.
Barlow, H. B. (1956). Retinal noise and absolute threshold. J. Opt. Soc. Am. 46, 634-
639.
Barlow, H. B. (1957). Purkinje shift and retinal noise. Nature 179, 255-256.
Barlow, H. B. (1958). Temporal and spatial summation in human vision at different
background intensities. J. Physiol. 141, 337-350.
Barlow, H. B. (1982). What causes trichromacy? A theoretical analysis using combfiltered
spectra. Vision Res. 22, 635-643.
Bowmaker, J. K. and Knowles, A. (1977). The visual pigments and oil droplets of the
chicken retina. Vision Res. 17, 755-764.
Bennett, A. T. and Cuthill, I. C. (1994). Ultraviolet vision in birds: what is its function?
Vision Res. 34, 1471-1478.
Brown, W. R. J. (1951). The influence of luminance level on visual sensitivity to color
differences. J. Opt. Soc. Am. 41, 684-688.
Cuthill, I. C., Bennet, T. D., Partridge, J. C. and Maier, E. J. (1999). Plumage
reflectance and the objective assessment of avian sexual dichromatism. Am. Nat.
153, 183-200.
Dartnall, H. J. A., Bowmaker, J. K. and Mollon, J. D. (1983). Human visual pigments:
microspectrophotometric results from the eyes of seven persons. Proc.R. Soc. B
220, 115-130.
De Vries, H. L. (1943). The quantum character of light and its bearing upon threshold
of vision, the differential sensitivity and visual acuity of the eye. Physica 10, 553-564.
Donner, K. (1989). The absolute sensitivity of vision: can a frog become a perfect
detector of light-induced and dark rod events? Physica Scripta 39, 133-140.
Donner, K. (1992). Noise and the absolute thresholds of cone and rod vision. Vision
Res. 32, 853-866.
Donner, K., Copenhagen, D. R. and Reuter, T. (1990). Weber and noise adaptation in
the retina of the toad Bufo marinus. J. Gen. Physiol. 95, 733-753.
Field, G. D. and Rieke, F. (2002). Nonlinear signal transfer from mouse rods to bipolar
cells and implications for visual sensitivity. Neuron 34, 773-785.
Firsov, M. and Govardovskii, V. (1989). Dark noise of visual pigments with differing
location of absorption maxima. Sensornye Sistemy 4, 25-34.
Fu, Y., Kefalov, V., Luo, D. G., Xue, T. and Yau, K. W. (2008). Quantal noise from
human red cone pigment. Nat. Neurosci. 11, 565-571.
Ghim, M. M. and Hodos, W. (2006). Spatial contrast sensitivity of birds. J. Comp.
Physiol. A 192, 523-534.
Goldsmith, T. H. and Butler, B. K. (2003). The roles of receptor noise and cone oil
droplets in the photopic spectral sensitivity of the budgerigar, Melopsittacus
undulatus. J. Comp. Physiol. A 189, 135-142.
Goldsmith, T. H. and Butler, B. K. (2005). Color vision of the budgerigar
(Melopsittacus undulatus): hue matches, tetrachromacy, and intensity discrimination.
J. Comp. Physiol. A 191, 933-951.
Gomez, D., Arnaud, G., Del Rey Granado, M., Bassoul, M. and Degueldre, D.
Perret, P. and Doutrelant, C. (2014). The intensity threshold of colour vision in a
bird, the blue tit. J. Exp. Biol. 217, 3775-3778.
Govardovskiĭ, V. I. (1983). On the role of oil drops in colour vision. Vision Res. 23,
1739-1740.
Govardovskii, V. I., Fyhrquist, N., Reuter, T., Kuzmin, D. G. and Donner, K. (2000).
In search of the visual pigment template. Vis. Neurosci. 17, 509-528.
Gover, N., Jarvis, J. R., Abeyesinghe, S. M. and Wathes, C. M. (2009). Stimulus
luminance and the spatial acuity of domestic fowl (Gallus g. domesticus). Vision Res.
49, 2747-2753.
Harmening, W. M., Nikolay, P., Orlowski, J. and Wagner, H. (2009). Spatial contrast
sensitivity and grating acuity of barn owls. J. Vision 9, 1-12.
Hart, N. S. (2001). The visual ecology of avian photoreceptors. Prog. Retin. Eye Res.
20, 675-703.
Howard, J. and Snyder, A. W. (1983). Transduction as a limitation on compound eye
function and design. Proc. R. Soc. B 217, 287-307.
Jarvis, J. R., Abeyesinghe, S. M., McMahon, C. E. and Wathes, C. M. (2009).
Measuring and modelling the spatial contrast sensitivity of the chicken (Gallus g.
domesticus). Vision Res. 49, 1448-1454.
Jones, C. D. and Osorio, D. (2004). Discrimination of oriented visual textures by
poultry chicks. Vision Res. 44, 83-89.
Johnsen, S. (2012). Absorption. In The Optics of Life (ed. S. Johnsen), pp. 104-115.
Princeton, NJ: Princeton University Press.
Kelber, A. and Lind, O. (2010). Limits of colour vision in dim light. Ophthalmic Physiol.
Opt. 30, 454-459.
Kram, Y. A., Mantey, S. and Corbo, J. C. (2010). Avian cone photoreceptors tile the
retina as five independent, self-organizing mosaics. PLoS ONE 5, e8992.
Lamb, T. D. and Simon, E. J. (1977). Analysis of electrical noise in turtle cones. J.
Physiol. 272, 435-468.
Lillywhite, P. G. and Laughlin, S. B. (1979). Transducer noise in a photoreceptor.
Nature 277, 569-572.
Lind, O. and Kelber, A. (2009a). The intensity threshold of colour vision in two species
of parrot. J. Exp. Biol. 212, 3693-3699.
Lind, O. and Kelber, A. (2009b). Avian colour vision: effects of variation in receptor
sensitivity and noise data on model predictions as compared to behavioural results.
Vision Res. 49, 1939-1947.
Lind, O. and Kelber, A. (2011). The spatial tuning of achromatic and chromatic vision
in budgerigars. J. Vision 11, 1-9.
Lind, O., Sunesson, T., Mitkus, M. and Kelber, A. (2012). Luminance-dependence of
spatial vision in budgerigars (Melopsittacus undulatus) and Bourke’s parrots
(Neopsephotus bourkii). J. Comp. Physiol. A 198, 69-77.
Lind, O., Chavez, J. and Kelber, A. (2014). The contribution of single and double
cones to spectral sensitivity in budgerigars during changing light conditions. J.
Comp. Physiol. A 200, 197-207.
Lisney, T. J., Rubene, D., Rózsa, J., Løvlie, H., Håstad, O. and Ödeen, A. (2011).
Behavioural assessment of flicker fusion frequency in chicken Gallus gallus
domesticus. Vision Res. 51, 1324-1332.
Osorio, D., Vorobyev, M. and Jones, C. D. (1999). Colour vision of domestic chicks.
J. Exp. Biol. 202, 2951-2959.
Osorio, D., Smith, A. C., Vorobyev, M. and Bucchanan-Smith, H. M. (2004).
Detection of fruit and the selection of primate visual pigments for color vision. Am.
Nat. 164, 696-708.
Maier, E. J. (1992). Spectral sensitivities including the ultraviolet of the passeriform
Leiothrix lutea. J. Comp. Physiol. A 170, 709-714.
Maier, E. J. and Bowmaker, J. (1993). Colour vision in the passeriform bird, Leiothrix
lutea: correlation of visual pigment absorbance and oil droplet transmission with
spectral sensitivity. J. Comp. Physiol. A 172, 295-301.
Prescott, N. B. and Wathes, C. M. (1999). Spectral sensitivity of the domestic fowl
(Gallus g. domesticus). Br. Poult. Sci. 40, 332-339.
Prins, N. and Kingdom, F. A. A. (2009). Palamedes: Matlab Routines for Analysing
Psychophysical Data. Available at: http://www.palamedestoolbox.org.
Rieke, F. and Baylor, D. A. (1998). Single-photon detection by rod cells of the retina.
Rev. Mod. Phys. 70, 1027-1036.
Rieke, F. and Baylor, D. A. (2000). Origin and functional impact of dark noise in retinal
cones. Neuron 26, 181-186.
Rose, A. (1942). The relative sensitivities of television pickup tubes, photographic film,
and the human eye. Proc. Inst. Radio Eng. 30, 293-300.
Rose, A. (1948). The sensitivity performance of the human eye on an absolute scale.
J. Opt. Soc. Am. 38, 196-208.
Schaefer, H. M., Schaefer, V. and Vorobyev, M. (2007). Are fruit colors adapted to
consumer vision and birds equally efficient in detecting colorful signals? Am. Nat.
169 Suppl. 1, S159-S169.
Schnapf, J. L., Nunn, B. J., Meister, M. and Baylor, D. A. (1990). Visual transduction
in cones of the monkey Macaca fascicularis. J. Physiol. 427, 681-713.
Schmid, K. L. and Wildsoet, C. F. (1998). Assessment of visual acuity and contrast
sensitivity in the chick using an optokinetic nystagmus paradigm. Vision Res. 38,
2629-2634.
Schneeweis, D. M. and Schnapf, J. L. (1995). Photovoltage of rods and cones in the
macaque retina. Science 268, 1053-1056.
Vorobyev, M. (2003). Coloured oil droplets enhance colour discrimination. Proc. Biol.
Sci. 270, 1255-1261.
TheJournalofExperimentalBiology
193
RESEARCH ARTICLE The Journal of Experimental Biology (2014) doi:10.1242/jeb.111187
Vorobyev, M. and Osorio, D. (1998). Receptor noise as a determinant of colour
thresholds. Proc. Biol. Sci. 265, 351-358.
Vorobyev, M., Osorio, D., Bennett, A. T. D., Marshall, N. J. and Cuthill, I. C. (1998).
Tetrachromacy, oil droplets and bird plumage colours. J. Comp. Physiol. A 183, 621-633.
Vorobyev, M., Brandt, R., Peitsch, D., Laughlin, S. B. and Menzel, R. (2001). Colour
thresholds and receptor noise: behaviour and physiology compared. Vision Res. 41,
639-653.
Warrant, E. J. (1999). Seeing better at night: life style, eye design and the optimum
strategy of spatial and temporal summation. Vision Res. 39, 1611-1630.
Wyszecki, G. and Stiles, W. S. (2000). Visual thresholds. In Color Science: Concepts
and Methods, Quantitative Data and Formulae, 2nd edn (ed. G. Wyszecki and W. S.
Stiles), pp. 525-544. New York, NY: Wiley.
Yebra, A., Garcia, J. A., Nieves, J. L. and Romero, J. (2000). Chromatic
discrimination in relation to luminance level. Color Res. Appl. 26, 123-131.