Color Perception in Children with Autism
Anna Franklin Æ Æ Paul Sowden Æ Æ Rachel Burley Æ Æ
Leslie Notman Æ Æ Elizabeth Alder
Published online: 1 May 2008
? Springer Science+Business Media, LLC 2008
is atypical in children with autism. In experiment 1,
accuracy of color memory and search was compared for
children with autism and typically developing children
matched on age and non-verbal cognitive ability. Children
with autism were significantly less accurate at color
memory and search than controls. In experiment 2, chro-
matic discrimination and categorical perception of color
were assessed using a target detection task. Children with
autism were less accurate than controls at detecting chro-
matic targets when presented on chromatic backgrounds,
although were equally as fast when target detection was
accurate. The strength of categorical perception of color
did not differ for the two groups. Implications for theories
on perceptual development in autism are discussed.
This study examined whether color perception
Autism ? Color ? Perception ? Categorization
Research into the cognition and perception of persons
with autism has found differences compared to control
groups on a range of tasks and for a range of domains.
For example, persons with autism have been shown to out
perform control groups on the embedded figures task
(Jolliffe and Baron-Cohen 1997; Shah and Frith 1983),
block design (Rumsey and Hamberger 1988; Shah and
Frith 1993); visual search (O’Riordan 2004; O’Riordan
et al. 2001; Plaisted et al. 1998a); and the reproduction of
impossible figures (Mottron et al. 1999). Enhanced per-
ception and discrimination has also been shown for pitch
processing, musical processing and processing of auditory
stimuli (e.g. Bonnel et al. 2003; Heaton et al. 1998;
Mottron et al. 2000), visuo-spatial perception (Caron
et al. 2004; Mitchell and Ropar 2004) and discrimination
of novel stimuli (Plaisted et al. 1998b), leading to theories
that those with autism show superior visual discrimination
(O’Riordan and Plaisted 2001) or an enhanced perceptual
functioning (e.g. Mottron and Burack 2001; Mottron et al.
2006) that may extend to a large number of perceptual
domains (Mottron et al. 2000). However, an impaired
perceptual ability in autism has been shown for some
other domains such as motion (e.g. Spencer et al. 2000;
Milne et al. 2002; but see also Bertrone et al. 2003,
Here we investigate whether there are also differences in
the color perception of those with and those without aut-
ism. Anecdotal evidence from parents, carers, teachers of
persons with autism and persons with autism themselves
suggests that children with autism may perceive color
differently to typically developing children. For example, a
parent of a child with autism describes her child’s color
obsessions and sensitivity to color:
‘‘he was given photocopied sheets of characters from
the film and would colour these in, from memory,
with complete accuracy....….George knew the col-
ours by heart, and never made a mistake’’ (Moore
2004, p. 68).
‘‘by and large he would eat only red food, and to this
day he uses ketchup to mask unwelcome colours. I
call his colour obsession ‘mild’ because I have heard
of far more extreme cases (Moore 2004, p. 153).’’
A. Franklin (&) ? P. Sowden ? R. Burley ? L. Notman ?
Department of Psychology, University of Surrey, Guildford,
Surrey GU2 5XH, England, UK
J Autism Dev Disord (2008) 38:1837–1847
Despite many such accounts suggesting that color per-
ception and cognition may be atypical in autism, there has
been little experimental investigation of whether this is the
case. Kovattana and Kraemer (1974) found that a non-
verbal group of children with autism preferred to use color
and size cues rather than form on a sorting task, whereas
there was no cue dominance in control groups. However,
Ungerer and Sigman (1987) found no significant differ-
ences in children’s sorting by function, form or color when
compared to a control group of typically developing chil-
dren. There is also a suggestion that children with autism
are advanced in their color naming (G. W. Schafer and T. I.
Williams, Unpublished manuscript). In addition, there is
some indirect evidence that there may be differences in
attention to color. For example, Brian et al. (2003), when
investigating inhibitory mechanisms in autism, found that
color unexpectedly produced a facilitation effect in persons
with autism but not controls. Brian et al. speculate that ‘in
autism, stimulus features such as color may be encoded too
readily, and thus are detected more easily than is typically
the case’ (p. 558). Greenaway and Plaisted (2005) find a
similar effect on a cueing task, where invalid color cues
resulted in greater costs for those with autism than controls.
Therefore, although anecdotal evidence suggests that
there may be some differences in the color perception of
children with autism and typically developing children,
there has been little direct experimental investigation of
this. There are theoretical reasons for addressing this
issue—for example, it allows us to test whether enhanced
domains. There are also clinical and practical reasons for
addressing this issue—gaining a better understanding of
color perception and cognition in autism may give insight
into color obsessions in autism and the way in which color
may shape those with autisms’ world.
In the current study we investigated the color percep-
tion of children with autism in two experiments where
children with high-functioning autism were compared to a
group of typically developing children matched on age
and non-verbal cognitive ability. In experiment 1, we
assessed accuracy of color memory and color search. In
experiment 2, we assessed accuracy and speed of chro-
matic discrimination using a target detection task. This
experiment also investigated the strength of categorical
influences on color perception by testing for categorical
perception of color.
Experiment 1: Color Memory and Search
in Children with Autism
In experiment 1 we assessed the hypothesis that color
memory and color search is atypical in children with
autism. Children with autism and typically developing
children matched on age and non-verbal cognitive ability
were compared on two tasks—a visual search task and a
delayed matching-to-sample task. On the visual search task
children were required to search a grid of colored squares
(15 distractors, 1 target) and identify the ‘odd-one-out.’ On
the delayed matching-to-sample task children were shown
a colored stimulus (target) and after a delay were asked to
identify the original stimulus (target) when paired with
another colored stimulus (foil). Accuracy of target identi-
fication was assessed on both tasks. To be able to get a
measure of color perception rather than just perception
generally, a comparison condition of stimuli differing in
form was also included for both tasks.
As color naturally differs along three dimensions—hue
(the ‘species’ or ‘ink’ of the color), saturation (colorfulness
rather like richness) and lightness (roughly equivalent to
the amount of light in the color/luminance), the colored
stimulus pairs in the current experiment differed along all
three dimensions. The colorimetric difference between
stimulus pairs was made small enough (in CIE L*a*b*,
perceptual color space) that errors on both tasks were
predicted. To be able to check that any differences in
accuracy were not specific to one region of the color space,
three sufficiently different regions of the color space were
sampled (red, green, yellow). Only three regions of color
space were sampled to ensure that the number of trials was
manageable for the child participants. Stimulus pairs were
taken from within each color region (e.g., red1 & red2) so
that verbal labeling of the colored target would not facili-
tate search (see Daoutis et al. 2006) or recognition memory
(see Franklin et al. 2005). The form stimuli were abstract
line drawn shapes. Stimulus pairs differed on one ele-
ment—with one line of one of the stimuli in the pair being
slightly curved whilst the curve of the corresponding line in
the other stimulus was more pronounced. The perceptual
difference between the two stimuli in a stimulus pair was
made small enough that errors on both tasks were pre-
dicted. To ensure that any differences in accuracy of form
perception were not specific to one form, three different
form stimulus pairs were used.
If enhanced perceptual functioning in autism extends
to color, then children with autism should be more
accurate when detecting colored targets on search and
memory tasks than typically developing children. If
children with autism actually have reduced perceptual
color functioning compared to typically developing
children, then accuracy will be lower than the control
group when detecting colored targets. If any differences
are specific to color, then no significant difference should
be found between children with autism and typically
developing children on accuracy of target identification
1838J Autism Dev Disord (2008) 38:1837–1847
Thirty-four children took part in the study, 20 with autism
and 14 typically developing children (all males). Children
Color Vision Test (Ishihara 1987). One child with autism
failed to complete the test and was excluded from the study,
resulting in a final sample of 19 children with autism. All
for children with autism and had been diagnosed by clini-
cians according to the criteria of DSM—IV (APA 1994).
of Attention Deficit Hyperactivity Disorder (ADHD).
Typically developing children and children with autism
were matched for non-verbal cognitive ability as assessed
by Raven’s Coloured Progressive Matrices (Sets A, Ab and
B, Raven et al. 1990), (t (31) = 0.10, p = 0.92) and
chronological age (t (31) = 1.71, p = 0.1), (see Table 1).
There were three colored stimulus pairs: red1 & red2;
yellow1 & yellow2; green1 & green2. The stimuli of each
pair differed in hue, lightness and saturation. For example
red1 and red 2 were different hues and were also different
in levels of lightness and saturation. The separation size of
stimuli in a pair in CIE (L*a*b*) perceptual color space
ranged from 12DE - 14DE, (see Table 2 for the Y, x, y
(CIE 1931) L*a*b* (CIE 1976) co-ordinates of stimuli).
There were three form stimulus pairs. These were
abstract line drawn shapes modified from Pick (1965).
The stimuli of each pair differed on one element—for
one stimulus in the pair one line was curved and for the
other stimulus in the pair the curve of the corresponding
line was more pronounced (see Fig. 1 for examples of
Stimuli for both sets filled a one inch square, and were
presented on a grey (Y = 127, x = 0.31, y = 0.30) back-
ground. For the visual search task, 16 stimuli were
presented in a grid arrangement. Fifteen stimuli were the
same (distractors) and there was one different stimulus
(target). There were four search grids for each stimulus pair
with each stimulus in the pair appearing as both the target
and the distractor. The location of the target was random-
ised across search grids.
Each participant completed both the visual search task and
the delayed matching-to-sample task, with the order of task
counterbalanced for each sample. The tasks were con-
ducted under Illuminant C (simulated natural daylight),
temp = 6,500 K.
Table 1 Chronological age and Raven’s Matrices raw scores for both
samples, Experiment 1
Autistic (N = 19)10.91.77–1329.55.915–36
Control (N = 14)9.82.27–1329.74.219–35
Table 2 Y, x, y and L*a*b* co-ordinates of stimuli
Red128.50.550.31 43.6155.20 25.18
Fig. 1 Examples of form stimulus pairs
J Autism Dev Disord (2008) 38:1837–18471839
Visual Search Task
Children were presented with a practise search grid and
were told to ‘point to the one that is different to all the
other ones’. Once it was clear that the children understood
the task, the children completed twelve color grids and
twelve shape grids in a randomised order. The child’s
response was recorded as either incorrect or correct.
Delayed Matching-to-sample Task
Children were presented with a stimulus (target) and were
told to remember it. After a 5-s delay, the stimulus was
covered with grey card. Following a further 5-s delay, a
stimulus identical to the target and the other stimulus in the
stimulus pair (foil) were presented. Children were asked to
‘point to the one that is the same as the one you just saw’.
There were four trials for each stimulus pair and each
stimulus appeared twice as the target and twice as the foil.
Children were given a practice and once it was clear that
the children understood the task, the children completed
twelve color trials and twelve shape trials in a randomized
order. The child’s response was recorded as either incorrect
The percentage of correct responses for the color trials and
the form trials on the delayed matching-to-sample and
visual search tasks was calculated. A three-way mixed
ANOVA with the repeated measures factors of Domain
(color/form) and Task (delayed matching-to-sample/visual
search) and an independent groups factor of Group (chil-
dren with autism/controls) was conducted on the accuracy
There was a significant main effect of Domain, with
85.7% [SD = 8.8] correct for color trials and 91.8%
[SD = 6.5] correct for form trials, [F(1,31) = 16.8,
was a significant interaction of Domain and Group, [F(1,
31), = 6.2, p\.05, gp
percentage accuracy for each group for color and form. All
other main effects and interactions were not significant
[largest F = 2.6, smallest p = 0.12].
Post hoc t tests (Bonferroni corrected significance level
p\.025) revealed a significant difference in accuracy
between children with autism and controls for color,
[t(31) = 2.7, p\.025], but not for form, [t(31) = 0.13,
p = 0.90].
A two-way mixed ANOVA with the repeated measures
factor of Color region (red/green/yellow) and the indepen-
dent groups factor of Group (children with autism/controls)
2= 0.35]. Key to the research question, there
2= 0.17]. Figure 2 shows the mean
was conducted on percentage accuracy. There was a signif-
icant difference in accuracy for green [mean = 81.6,
SD = 1.3], yellow [mean = 81.6, SD = 12.9] and red
[mean = 92.3,
SD = 15.5],
compared to red [p\.005]. As identified in the previous
ANOVA, there was a significant difference in accuracy of
children with autism and typically developing children,
[F(1,31) = 8.74, MSE = 218, p\.01, gp
ever, there was no significant interaction between Color
region and Group, [F(2,62) = 1.0, p = 0.37].
[F(2,62) = 9.5,
2= 0.22]. How-
Experiment 1 assessed accuracy of color perception in
children with autism using a visual search and delayed
matching-to-sample task. Significant differences in the
accuracy of both color memory and color search were
found between children with autism and controls matched
on non-verbal cognitive ability and age. Children with
autism were significantly less accurate at identifying a
colored target on the two tasks compared to controls.
Importantly, this was found for all three color regions (red,
green and yellow), suggesting that this pattern is not
restricted to certain regions of the color space. Also
importantly, there was no difference in accuracy between
the two groups for the form condition, suggesting that the
differences between the two groups were due to color and
not due to more general differences in perception or
Children with autism
Fig. 2 Percentage Accuracy (+/- 1se) combined for visual search
and delayed matching-to-sample tasks, on color and form discrim-
ination for children with autism and typically developing children
1840J Autism Dev Disord (2008) 38:1837–1847
cognition. Experiment 2 further investigated this effect,
using a task which is intended to measure color discrimi-
nation more directly than the tasks in experiment 1.
Experiment 2 also assessed the possibility that there are
differences between children with autism and typically
developing children in the strength of categorical influ-
ences on color perception.
Experiment 2: Chromatic Discrimination
and Categorical Perception of Color
In experiment 1, children with autism had less accurate
color perception on two tasks compared to typically
developing children. One aim of experiment 2 was to see
whether the findings of experiment 1 could be replicated
using a target detection task. The target detection task was
originally developed to assess color discrimination in
infants (Franklin et al. 2005), although it has since been
modified for use with adult samples, and has been revealed
to be a task that is sensitive to supra-threshold differences
in color discrimination (e.g. Drivonikou et al. 2007). The
task involves the detection of a colored target when pre-
sented on a colored background, and as the target is defined
purely by the chromatic difference of the target and the
background, efficiency of target detection is related to the
perceptual similarity of the target and background colors.
Unlike the delayed matching-to-sample or visual search
task, target detection simply involves the detection of a
chromatically defined edge (rather than the memory or
comparison of targets and distractors), therefore the task is
thought to tap chromatic discrimination more directly than
the other two tasks. If children with autism are also less
accurate at identifying targets than typically developing
children on this task, this would give further support to the
hypothesis that children with autism have less accurate
chromatic discrimination than typically developing chil-
dren. As the task is computerized, the speed of target
identification can also be recorded, which allows an
assessment of whether chromatic discrimination in autism
is also slower as well as less accurate compared to typically
A second aim of the current experiment was to inves-
tigate the strength of categorical influences on color
perception in children with autism. One way of investi-
gating the activation and interaction of different levels of
processing in autism has been to investigate categorization
in autism. Such studies have assessed the influence or
formation of category prototypes (Dunn et al. 1996; Klin-
ger and Dawson 2001; Molseworth et al. 2005), and the
strength of categorical influences on facial expressions
(Teunisse and de Gelder 2001) and ellipse width (Soulie `res
et al. 2007). These studies have provided mixed support for
the hypothesis that categorization in autism is atypical, and
only two of these studies (Molesworth et al. 2005; Sou-
lie `res et al. 2007) match groups on IQ. In the Molesworth
et al. (2005) study, those with autism formed artificial
animal categories and prototypes to the same extent as
those without autism. Soulie `res et al. (2007) investigated
the ability of those with autism to categorize a continuum
of ellipse widths into two categories and in addition
assessed the influence that these categories had on dis-
crimination. Although, those with autism showed typical
classification curves (they divided the continuum in the
same way as those without autism), the influence of this
categorization on discrimination was shown to be weaker
for those with autism than those without. Only those
without autism showed a ‘categorical perception’ effect—
facilitated discrimination of stimuli that cross a category
boundary (between-category) compared to equivalently
spaced stimuli from the same category (within-category),
(see Harnad 1987). As ‘categorical perception’ is also
typically found for the domain of color (e.g. Bornstein and
Korda 1984), the second aim of the current experiment was
to assess whether the reduced categorical perception effect
in autism found by Soulie `res et al. could be generalized to
other perceptual domains. Therefore, here we test for cat-
egorical perception of color using the target detection task.
The target detection task has previously been used to show
categorical perception of color across the blue-green cat-
egory boundary in adults (Drivonikou et al. 2007) and we
test the same category boundary here.
If categorical perception (CP) of color is shown on the
target detection task, target detection should be faster and/
or more accurate when the target is shown on a different-
category (between-category condition) than same-category
(within-category condition) background, when between-
and within-category stimulus chromatic separation sizes
are equated. The difference in target detection time for
within- and between-category conditions indicates the
strength of the CP effect. Unlike other tasks (e.g. triadic
judgment tasks), the task does not ask participants to make
explicit judgments of whether the colors are ‘same’ or
‘different’, and unlike other tasks (such as delayed
matching-to-sample tasks) the detection of the target is not
aided by a verbal labeling strategy. This ensures that CP
and any differences between those with autism and those
without autism are likely to be due to perceptual processes.
Thirty children took part in the study, 16 with autism and
14 typically developing children (all males). Children were
J Autism Dev Disord (2008) 38:1837–18471841
screened for color vision deficiencies using the Ishihara
Color Vision Test (Ishihara 1987). One child with autism
who failed to complete the color vision test was excluded
from the study, and one child was excluded for performing
at chance, resulting in a final sample of 14 children with
autism. All children with autism were high-functioning,
attended schools for children with autism and had been
diagnosed by clinicians according to the criteria of DSM—
IV (APA 1994). None of the children in either group had
received a diagnosis of ADHD. Typically developing
children and children with autism were matched for non-
verbal cognitive ability as assessed by Raven’s Standard
Progressive Matrices (Sets A, B, C, D, E, Raven et al.
1992) [t (26) = .912, p = .37] and chronological age [t
(26) = 1.54, p = .14], (see Table 3).
Stimuli were presented on a 1600Vision Master pro-1314
CRT monitor, and the chromaticity and luminance of the
stimuli were verified with a Cambridge Research Systems
(Rochester, UK) ColorCal instrument. Responses were
made on a games pad. Participants were seated 50 cm away
and at eye-level to the monitor, and the experimental
session was conducted in a darkened room.
Stimuli and Design
Categorical perception of color was tested for across the
blue-green category boundary and within- and between-
category chromatic separation sizes were equated using
stimuli from a standardized color space that is frequently
used in studies of color-CP (the Munsell Color Order
System). Four stimuli were taken from the blue-green
region of the Munsell color space, with adjacent stimuli
separated by 2.5 Munsell hue units, and stimuli constant in
saturation (Munsell chroma = 7) and lightness (Munsell
value = 8). The stimuli straddled the well-established
blue-green category boundary (7.5BG) and there were
three stimulus pairs: within-category green; between-cate-
gory; within-category blue (see Fig. 3). Table 4 gives the
Y, x, y co-ordinates (CIE 1931) of the four stimuli. The
colored target (diameter 30 mm, visual angle 3.5?)
appeared in one of 12 un-marked locations in a ring around
a central fixation cross. The colored target was shown on a
colored background that filled the entire screen. For each
stimulus pair, the target appeared to the left or the right of
the central fixation cross for an equal number of trials.
There were 16 trials for each stimulus pair, with each
stimulus in the pair appearing for an equal number of trials
as the target and the background. This resulted in 48 trials
and these were presented in a randomised order. Before the
onset of each trial, the white central fixation cross pre-
sented on a black background was shown for 1,000 ms,
followed by the background and target. The target was
shown for 250 ms and the background remained until the
participant had made their response.
Participants were told that the aim of the task was to judge
whether a circle appears on the left or the right of the
screen. They were instructed to look at the cross in the
middle of the screen and that they should press the left
button if the circle appears on the left, and the right button
if the circle appears on the right. They were given a
practise session of 24 trials and after these trials if it was
clear that the task instructions were understood, the 48
experimental trials were started. The experimental program
recorded speed and accuracy of response.
The mean percentage accuracy and the mean reaction time
(ms) on accurate trials were calculated for within-category
Table 3 Chronological age and Raven’s Matrices raw scores for both
samples, Experiment 2
Autistic (N = 14)11.930.73 11–1336.297.91 14–45
Control (N = 14)12.290.47 12–1333.86 6.0618–42
Within-category Between-category Within-category
Fig. 3 Between-category and within-category blue/green stimulus
pairs. Stimulus pairs are separated by 2.5 Munsell hue units and all
stimuli are at constant saturation and lightness (Munsell chroma = 7,
Munsell value = 8). The dashed line indicates the category boundary
Table 4 Munsell codes, Y, x, y (CIE 1931) and L*u*v* (CIE 1976)
co-ordinates of stimuli. White of monitor: Y = 60.26 cd/m2,
x = 0.327, y = 0.339
1.25B 7/825.95 0.2220.29471.60-53.96-37.94
8.75BG 7/825.95 0.2260.31071.60 -55.57-28.44
1842 J Autism Dev Disord (2008) 38:1837–1847
and between-category conditions, for children with autism
and for controls. A two-way, mixed design ANOVA with
an independent groups factor of Group (autism/controls)
and a repeated measures factor of Category (within/
between) was conducted on mean accuracy and also on
mean reaction time on accurate trials.
Figure 4 shows the mean percentage accuracy for children
with autism and for typically developing children, for
within-category and between-category conditions.
As is apparent in Fig. 4, there was a significant main
effect of Group, with less accurate target detection for
children with autism [mean = 62.17%, SD = 13.56] than
controls [mean = 78.68%, SD = 15.92], [F(1,26) = 8.72,
MSE = 437.9, p\.01, gp
and interactions were not significant [largest F = 1.03,
smallest p = 0.32]. One sample t-tests (test value = 50%)
confirmed that both groups were significantly above
chance: children with autism, [t(13) = 3.36, p\.01];
controls, [t(13) = 6.74, p\.001].
2= 0.25]. All other main effects
Figure 5 gives the mean reaction time on correct trials for
children with autism and typically developing children on
within-category and between-category conditions.
target detection for between-category [mean = 1049 ms,
SD = 347]thanwithin-category
2257 ms, SD = 837], [F(1,26) = 86.02, MSE = 237567,
were not significant [largest F = 2.39, smallest p = 0.13].
2= 0.77]. All other main effects and interactions
Accuracy/Reaction Time Trade Off
An ANCOVA with Group (autism/typically developing)
and Category (within/between) as factors was conducted on
mean percentage accuracy with reaction time as a covari-
ate. Having reaction time as a covariate did not change the
pattern of results: there was still a significant main effect of
group [F(1,25) = 5.91, MSE = 94.5, p\.05], and no
other significant main effects or interactions [largest
F = .52, smallest p = .48]. An equivalent ANCOVA was
conducted on reaction time with mean percentage accuracy
as a covariate. Again, having mean percentage accuracy as
a covariate did not change the pattern of results—there was
still a significant main effect of category [F(1,25) = 86.44,
MSE = 229090, p\.001], and no other significant main
effects or interactions [largest F = .62, smallest p = .44].
Experiment 2 compared children with autism and typically
developing children matched on age and non-verbal
Children with autism
Fig. 4 Percentage accuracy (+/- 1se) of target detection on within-
and between-category trials, for children with autism and typically
Reaction Time (ms)
Children with autism
Fig. 5 Reaction time (ms, +/- 1se) for accurate target detection on
within- and between-category trials, for children with autism and
typically developing children
J Autism Dev Disord (2008) 38:1837–18471843
cognitive ability, in their accuracy and speed of color
discrimination and the strength of categorical perception of
color, using a target detection task. Children with autism
were less accurate at target detection than those without
autism. This lower accuracy for children with autism
compared to the control group was found for both within-
category and between-category targets and backgrounds.
However, when children did accurately detect the colored
target they were no slower than the control group in doing
this. Categorical perception was not found for accuracy of
target detection, but was found for speed of target detec-
tion, with faster detection of targets when presented on
different- than same-category backgrounds. Both children
with autism and the typically developing children showed
categorical perception of color, and the strength of this
category effect did not differ for the two groups of
The findings of experiment 2 therefore replicate the
finding in experiment 1 of less accurate color perception in
those with autism compared to matched controls, and
extend this finding to a task that measures chromatic dis-
crimination more directly. The results also suggest that
children with autism are not only less accurate at dis-
criminating colors within a color category, but are also less
accurate at between-category discriminations than typically
developing children. That categorical perception of color
was found for the speed of target detection but not the
accuracy is not unusual—CP is not always found on all
measures and this pattern of CP affecting speed but not
accuracy has been found previously (e.g. Franklin et al.
2005). Despite the finding that children with autism have
less accurate color perception than controls, these children
have typical categorical perception of color. This suggests
that the lack of categorical perception in children with
autism, in Soulie `res et al.’s (2007) study of ellipse dis-
crimination, does not generalise to the domain of color.
One possible reason for this may be the difference in the
nature of the categories being investigated. For example,
the categories in Soulie `re et al.’s investigation were learnt
during a classification task according to an arbitary
boundary. However, perceptual color categories are likely
to already be well learned and are more likely than Sou-
lie `res et al.’s ellipse categories, to be at least partially
biologically constrained. For example, color categories
have universal prototypes (e.g. Regier et al. 2005), cate-
gorical perception of color is found in infants as young as
four-months (e.g. Bornstein et al. 1976; Franklin and
Davies 2004; Franklin et al. 2005), and an ERP study of
color-CP has revealed category effects appearing as early
100–120 ms on a visual oddball task (Holmes et al. 2008).
Therefore, reduced categorical perception in autism may be
limited to learned categories that are possibly mediated by
more top-down mechanisms (Sowden and Schyns 2006).
Further research, testing for categorical perception in aut-
ism for other domains (e.g. orientation, Quinn 2004) is
needed to establish when categorization is atypical in
autism. Gaining a better understanding of this may lead to a
greater understanding of the activation and interaction of
different levels of processing in autism.
On visual search, memory (experiment 1) and target
detection tasks (experiment 2) children with autism were
less accurate than typically developing children at detect-
ing the differences between colors. The difference between
high functioning children with autism and typically
developing children in accuracy of color perception
appears to be robust. For example, the effect is found on
three different tasks tapping three different perceptual
processes (color search, color memory, chromatic edge
detection), in various areas of the color space (red, green,
yellow in experiment 1 and blue-green in experiment 2),
and when color varies along all three colorimetric dimen-
sions (hue,lightness and
experiment 1) or when color varies just in hue (experiment
2). Despite less accurate color perception, the strength of
categorical influences on color perception does not appear
to be atypical in autism and children with autism show
categorical perception across the blue-green boundary to
the same extent as the control group.
These findings of less accurate color perception appear
to be at odds with anecdotal evidence of strong color
obsessions in children with autism (e.g. Moore 2004), and
the suggestion that children with autism have advanced
color naming (G. W. Schafer and T. I. Williams, Unpub-
lished manuscript). However, there could be other potential
explanations for why children with autism may have strong
color obsessions or advanced color naming, which are
based on social or conceptual rather than perceptual
accounts. The findings also do not support the hypothesis
that enhanced color perception can account for the color
facilitation effect on a negative priming task for children
with autism but not controls (Brian et al. 2003), and the
greater cost of invalid color cues for those with autism than
controls (Greenaway and Plaisted’s 2005). Studies that
explore more complex aspects of attention to color are
needed to further investigate the cause of these effects. The
current experiments also do not provide evidence that
‘enhanced perceptual functioning’ or ‘reduced categoriza-
tion’ in autism extends to the domain of color. This
suggests that these phenomena may apply selectively to
certain perceptual domains but not others.
So why do children with autism appear to be less
accurate at color memory, search and chromatic target
1844J Autism Dev Disord (2008) 38:1837–1847
detection than controls? One potential explanation is that
the difference arises from differences in the anatomical and
functional organization of the brain in autism. How the
human brain processes color is not fully understood and the
precise areas that are involved are controversial (Engel and
Funanski 2001). However, a generally accepted basic
account of color processing holds that color vision starts in
the retina, where activation of cones with photopigments
sensitive to short (S), medium (M) and long (L) wave-
lengths of light leads to two opponent processes—a red-
green axis (L-M) and a blue-yellow axis (S - (L + M)
(e.g. De Valois and De Valois 1993). Then, in essence,
Parvocellular and Koniocellular cells in the Lateral
Geniculate Nucleus code for chromaticity, and Magnocel-
lular cells for luminance, giving different pathways to the
visual cortex (e.g. Lee et al. 1990; Livingstone and Hubel
1988; although see also Schiller and Logothetis 1990).
Various areas of the visual cortex have been implicated in
color vision, and color-selective neurons have been found
in areas V1 and V2 (e.g. Livingstone and Hubel 1984), V4
(e.g. Zeki et al. 1991)/V8 (Hadjikhani et al. 1998). Fol-
lowing this, a network of many different brain areas is
thought to be involved (e.g. Gulyas and Roland 1994),
mainly in the ventral occipito-temporal cortex (e.g. Beau-
champ et al. 1999), although some have argued that there is
also some dorsal activation (Claeys et al. 2004). The pat-
tern of results in the current study could arise from
disruption to any one or more of these different biological
and neurological processes. Further studies are needed to
explore this. For example, a threshold discrimination task
that measured just-noticeable differences using stimuli
along blue-yellow and red-green cone excitation axes could
indicate whether there are differences in one or both of the
opponent processes for children with autism and typically
developing children. Event-related potential studies may
give an indication of whether there are any differences in
the time course of color processing which may help to
indicate the stage(s) at which color processing is disrupted.
Likewise, fMRI studies of color processing in autism could
highlight any differences in the pattern of neural activation
involved in color processing. However, there could be
explanations for the pattern of results in the current study
other than a biological account. For example, less accurate
color perception could arise from various conceptual,
social or cultural factors (such as a restricted use of color in
educational contexts). Therefore, further research which
investigates the broader context of color for those with
autism is also needed to explore these alternative accounts.
In conclusion, two experiments provide converging
evidence that children with autism are less accurate at color
perception than controls matched on age and non-verbal
cognitive ability. This effect appears to be robust—it is
found on three tasks that tap different aspects of color
perception and it is found for various regions of the color
space. Despite differences in accuracy of color perception,
categorical perception of color appears to be intact in
autism, with an equivalent amount of color CP in children
with autism compared to the controls. It is clear that there
is a long way to go to fully understand the perception and
cognition of color in children with autism. Nevertheless
this study represents an important first step to under-
standing how these children interact with and perceive the
world of color.
from discussions with Graham Schafer about color naming abilities in
children with autism. We are grateful to the schools and children who
were involved in this research, to Lynsey Mahony for assistance with
some of the data collection and to Hollie Boulter and Eleanor Rees for
helpful discussions about the research. We also owe thanks to Sheila
Franklin for providing the anecdotal evidence of color obsessions in
those with autism. This research was funded by a UREC Pump-
priming grant to the first author.
The idea for this research originated in part
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