In general, European languages divide the spectrum like pictured be...
There are plenty of interesting differences between languages when ...
There has been a long standing debate surrounding the concept of li...
In the cited studies they use the same verbal interference method u...
Remember, *"within category”* means that the 2 bottom squares (the ...
#### ANOVA ANOVA stands for *Analysis of Variance*. It is a collec...
Russian blues reveal effects of language
on color discrimination
Jonathan Winawer*
, Nathan Witthoft*
, Michael C. Frank*, Lisa Wu
, Alex R. Wade
, and Lera Boroditsky
*Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139-4307;
Department of Neurology, David Geffen
School of Medicine, University of California, Los Angeles, CA 90095-1769;
Brain Imaging Center, Smith–Kettlewell Eye Research Institute, San Francisco, CA
94115; and
Department of Psychology, Stanford University, Stanford, CA 94305
Communicated by Gordon H. Bower, Stanford University, Stanford, CA, March 7, 2007 (received for review September 22, 2006)
English and Russian color terms divide the color spectrum differ-
ently. Unlike English, Russian makes an obligatory distinction
between lighter blues (‘‘goluboy’’) and darker blues (‘‘siniy’’). We
investigated whether this linguistic difference leads to differences
in color discrimination. We tested English and Russian speakers in
a speeded color discrimination task using blue stimuli that spanned
the siniy/goluboy border. We found that Russian speakers were
faster to discriminate two colors when they fell into different
linguistic categories in Russian (one siniy and the other goluboy)
than when they were from the same linguistic category (both siniy
or both goluboy). Moreover, this category advantage was elimi-
nated by a verbal, but not a spatial, dual task. These effects were
stronger for difficult discriminations (i.e., when the colors were
perceptually close) than for easy discriminations (i.e., when the
colors were further apart). English speakers tested on the identical
stimuli did not show a category advantage in any of the conditions.
These results demonstrate that (i) categories in language affect
performance on simple perceptual color tasks and (ii) the effect of
language is online (and can be disrupted by verbal interference).
categorization cross-linguistic Whorf
if ferent languages divide c olor space differently. For exam-
ple, the English ter m ‘‘blue’’ can be used to describe all of
the c olors in Fig. 1. Unlike English, Russian makes an obligatory
distinction between lighter blues (‘‘goluboy’’) and darker blues
(‘‘sin iy’’). Like other basic c olor words, ‘‘sin iy’’ and ‘‘goluboy’’
tend to be learned early by Russian children (1) and share many
of the usage and behavioral properties of other basic color words
(2). There is no single generic word for ‘‘blue’’ in Russian that
can be used to describe all of the colors in Fig. 1 (nor to
adequately translate the title of this work from English to
Russian). Does this difference between languages lead to dif-
ferences in how people discriminate colors?
The question of cross-linguistic differences in color perception
has a long and venerable history (e.g., refs. 3–14) and has been
a cornerstone issue in the debate on whether and how much
language shapes thinking (15). Previous studies have found
cross-linguistic differences in subjective color similarity judg-
ments and c olor confusabilit y in memory (4, 5, 10, 12, 16). For
example, if two c olors are called by the same name in a language,
speakers of that language will judge the two colors to be more
similar and will be more likely to confuse them in memory
c ompared with people whose language assigns different names
to the two c olors. These cross-linguistic differences develop early
in children, and their emergence has been shown to coincide with
the acquisition of color terms (17). Further, cross-linguistic
dif ferences in similarity judgments and recognition memory can
be disrupted by direct verbal interference (13, 18) or by indirectly
preventing subjects from using their normal naming strategies
(10), suggesting that linguistic representations are involved
online in these kinds of color judgments.
However, evidence from memory studies and subjective sim-
ilarit y ratings has left some critics unconvinced (19, 20). Pinker
(19) summarizes the critiques as follows:
Most of the experiments have tested banal ‘‘weak’’
versions of the Whorfian hypothesis, namely that words
can have some effect on memory or categorization. . . .
In a typical experiment, subjects have to commit paint
chips to memory and are tested with a multiple-choice
procedure. In some of these studies, the subjects show
slightly better memory for colors that have readily
available names in their language. . . . A ll [this] shows is
that subjects remembered the chips in two forms, a
non-verbal visual image and a verbal label, presumably
because two types of memory, each one fallible, are
better than one. In another type of experiment subjects
have to say which two of three c olor chips go together;
they often put the ones together that have the same
name in their language. Again, no surprise. I can imagine
the subjects thinking to themselves, ‘‘Now how on earth
does this guy expect me to pick two chips to put
together? He didn’t give me any hints, and they’re all
prett y similar. Well, I’d probably call these t wo ‘green’
and that one ‘blue,’ and that seems as good a reason to
put them together as any.’’
Because previous cross-linguistic comparisons have relied on
memory procedures or subjective judgments, the question of
whether language affects objective c olor discrimination perfor-
mance has remained. Studies testing only c olor memory leave
open the possibility that, when subjects make perceptual dis-
criminations among stimuli that can all be viewed at the same
time, language may have no influence. In studies measuring
subjective similarity, it is possible that any language-congr uent
bias results from a conscious, strategic decision on the part of the
subject (19). Thus, such methods leave open the question of
whether subjects’ normal ability to discriminate colors in an
objective procedure is altered by language.
Here we measure c olor discrimination perfor mance in two
language groups in a simple, objective, perceptual task. Subjects
were simultaneously shown three color squares arranged in a
triad (see Fig. 1) and were asked to say which of the bottom two
c olor squares was perceptually identical to the square on top.
This design combined the advantages of previous tasks in a
way that allowed us to test for the effects of language on color
perception in an objective task, with an implicit measure and
min imal memory demands.
First, the task was objective in that subjects were asked to
provide the correct answer to an unambiguous question, which
they did with high accurac y. This feature of the design addressed
the possibility that subjects rely only on linguistic representations
when faced with an ambiguous task that requires a subjective
Author contributions: J.W., N.W., M.C.F., A.R.W., and L.B. designed research; J.W., N.W.,
M.C.F., and L.W. performed research; J.W., N.W., L.W., and L.B. analyzed data; and J.W.
wrote the paper.
The authors declare no conflict of interest.
To whom correspondence should be addressed. E-mail:
© 2007 by The National Academy of Sciences of the USA
May 8, 2007
vol. 104
no. 19 www.pnas.orgcgidoi10.1073pnas.0701644104
judgment. If linguistic representations are only used to make
subjective judgments in ambiguous tasks, then effects of lan-
guage should not show up in an objective unambiguous task with
a clear correct answer.
Sec ond, all stimuli involved in a perceptual decision (in this
case, the three color squares) were present on the screen
simult aneously and remained in full view until the subjects
responded. This allowed subjects to make their decisions in the
presence of the perceptual stimulus and with minimal memory
Finally, we used the implicit measure of reaction time, a subtle
aspect of behavior that subjects do not generally modulate
ex plicitly. Although subjects may decide to bias their decisions in
choosing between two options in an ambiguous task, it is unlikely
that they explicitly decide to take a little longer in responding in
some trials than in others.
In summary, this design allowed us to test subjects’ discrim-
ination performance of a simple, objective perceptual task.
Further, by asking subjects to perform these perceptual discrim-
inations with and without verbal interference, we are able to ask
whether any cross-linguistic differences in color discrimination
depend on the online involvement of language in the course of
the task.
The questions asked here are as follows. Are there cross-
linguistic differences in color discrimination even for simple,
objective, perceptual discrimination tasks? If so, do these dif-
ferences depend on the online involvement of language? Prev i-
ous studies with English speakers have demonstrated that verbal
interference changes English speakers’ performance in speeded
c olor discrimination (21) and in visual searching (22, 23) across
the English blue/green boundary. If a c olor boundary is present
in one language but not another, will the two language groups
dif fer in their perceptual discrimination performance across that
boundary? Further, will verbal interference affect only the
performance of the language group that makes this linguistic
Here we tested English and Russian speakers in an objective
c olor discrimination task across a color boundary that exists in
Russian but not in English. Twenty color stimuli spann ing the
Russian siniy/goluboy range were used (Fig. 1). Subjects were
shown colors arranged in a triad; their task was to indicate as
quickly and ac curately as possible which of the t wo bottom color
squares was identical to the top square. In some trials the
distracter square was from the same Russian category as the
match (i.e., both were goluboy or both were siniy); these were
called ‘‘within-category’’ trials. In other trials the match and the
distracter fell into different Russian categories (i.e., one was
goluboy and one was siniy); these were called ‘‘cross-category’’
trials. For English speakers, all of the colors in all trials fell into
the same basic linguistic category, namely, blue.
If linguistic ef fects on color discrimination are specific to the
categories encoded in a speaker’s language, then Russian
speakers should make faster cross-category discriminations
than within-category discriminations, a category advant age.
For English speakers, it should not matter whether c olors fall
into the same or dif ferent linguistic categories in Russian, so
they should not show any such dif ferences.
Further, if linguistic processes play an active, online role in
perceptual tasks (10), then a verbal dual task, but not a nonlin-
guistic dual task, should diminish the goluboy/sin iy category
advant age found in Russian speakers. To evaluate this possibil-
it y, subjects performed the color discrimination task under three
c onditions: a normal viewing, no-interference condition in which
there was no dual task; a verbal-interference condition, in which
subjects silently rehearsed digit strings while simultaneously
c ompleting the color discrimination trials; and a control, spatial-
interference condition, in which subjects maintained a spatial
pattern in memory while completing color discrimination trials.
The spatial-interference control c ondition was used to examine
whether any differences between the baseline condition and
verbal-interference c ondition were specific to language, or
whether they were due to nonspecific effects of any dual task.
Finally, we had previously found (unpublished work) that lin-
guistic categorie s are more likely to play a role in perceptual tasks
that are more difficult (e.g., ones that involve finer discriminations).
To explore this finding with a new set of color stimuli and speakers
of a different language, we included color discriminations that were
easier (in which the target and distracter color squares were
perceptually dissimilar, the ‘‘far-color comparisons’’) and discrim-
inations that were harder (in which the target and distracter color
squares were perceptually closer, the ‘‘near-color comparisons’’).
Boundaries. To determine each subject’s linguistic color bound-
ary within the range of blues used in this work, we administered
a brief c olor classification task at the end of the experiment
(af ter the main color discrimination blocks). Subjects were asked
to classify each color square used in this work as either goluboy
or siniy (for Russian speakers) or light blue or dark blue (for
English speakers). All subjects classified the lightest stimulus
(stimulus 1 in Fig. 1) as goluboy or light blue and stimulus 20 as
sin iy or dark blue. Each subject’s boundary was identified as the
transition point in these classification responses. If the transition
fell between two stimuli or was ambiguous, the slower reaction
time was used to disambiguate the boundary, because colors
closest to boundaries tend to be categorized more slowly in
simple classification tasks (e.g., ref. 24). The locations of the
goluboy/sin iy boundary (Russian speakers) and the light blue/
dark blue boundary (English speakers) were nearly identical:
8.7 2.2 vs. 8.6 2.5, respectively (mean SD).
Analysis. Each subject’s data were analyzed relative to their own
linguistic boundary. Trials were classified as within-category if
the test stimuli fell on the same side of that subject’s boundary
01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20
Fig. 1. The 20 blue colors used in this study are shown at the top of the figure.
An example triad of color squares used in this study is shown at the bottom of
the figure. Subjects were instructed to pick which one of the two bottom
squares matched the color of the top square.
Winawer et al. PNAS
May 8, 2007
vol. 104
no. 19
(e.g., both goluboy or both light blue) and were classified as
cross-category if they fell on opposite sides of the boundary or
if one of the two stimuli was the boundary. For each subject, the
n ine near-color and the nine far-color comparisons closest to
that subject’s boundary were included in the analysis. This
ensured that the set of stimuli used was centered relative to each
subject’s category boundary.
Additionally, trials were excluded if the response to the
interference stimulus was incorrect during the interference
blocks, if the response to the color task was incorrect, or if the
reaction time for the color discrimination was 3 sec; 12% of
trials were so excluded. Subjects were excluded entirely from
analysis if the above criteria resulted in loss of 25% or more of
the trials, leading to the exclusion of three English and five
Russian speakers.
Summary of Results. Russian speakers showed a category advan-
t age when tested without interference, whereas English speakers
did not (Fig. 2). The category advantage found for Russian
speakers was disrupted by verbal, but not spatial, interference.
English speakers did not show a category advantage in any
c ondition. Further, effects of language were most pronounced
for more difficult discriminations (i.e., the near-color compari-
sons) (Fig. 3).
Detailed Analyses. Subjects were much faster at far-c olor discrim-
inations than near-color discriminations. This effect was re-
flected in separate 2 3 2 repeated-measures ANOVAs
calculated for each language group, with the factors of distance
(near color vs. far color), interference (none vs. spatial vs.
verbal), and category (bet ween vs. within). For each group, there
was a highly significant main effect of distance: in Russian
speakers [926 vs. 1,245 msec, near color vs. far color; F (1, 20)
267; P 0.001] and English speakers [800 vs. 1,078 msec; F (1,
20) 144.1; P 0.001]. Additionally, a mixed-design ANOVA
using the above three factors as repeated measures and language
as a between-subjects factor showed that Russian speakers were
slower overall than English speakers [1,085 vs. 938 msec; F (1,
40) 6.93; P 0.012]. This difference might be due to the fact
that the Russian speakers we tested had less experience than the
English speakers in using computers or t aking part in experi-
ments. The mean and SE for each condition are included in
Table 1.
More critical to our hypothesis, the 2 3 2 ANOVA of the
Russian speakers showed that the performance in cross-category
vs. within-category trials was modulated by the interference
c ondition: there was a category advantage under both the no-
interference and the spatial-interference conditions, but not
under the verbal interference condition (Fig. 2) [category
interference interaction; F (2, 40) 5.3; P 0.009]. This effect
was completely due to the near-c olor condition (Fig. 3), sup-
ported by a significant three-way interaction among category,
interference, and distance [F (2, 40) 3.3; P 0.049]. This
finding, that language plays a role only in more difficult tasks
(near-c olor vs. far-color c omparisons, for example), is consistent
with our findings for the blue/green boundary in English in which
a category advantage was observed for harder, but not (unpub-
lished work) easier, discriminations. There were no other sig-
n ificant main effects or interactions in this analysis.
To explore in more det ail the interaction among distance,
category, and interference, several planned t tests were c on-
ducted under each of the separate conditions. In near-color
trials, Russian speakers showed a category advantage without
interference [1,164 vs. 1,288 msec; t (20) 2.59; P 0.0176] and
with spatial interference [1,162 vs. 1,270 msec; t (20) 2.18; P
0.041] but a trend toward a category disadvantage with verbal
interference [1,325 vs. 1,260 msec; t (20) 1.87; P 0.076].
Moreover, the category advantage was significantly larger in no
interference blocks than in verbal interference blocks [124 vs.
64 msec; t (20) 2.93; P 0.0082] and in spatial-interference
blocks than in verbal-interference blocks [109 vs. 64 msec; t
(20) 3.23; P 0.004]. No difference in category advant age was
found between the spatial- and no-interference conditions [t
There is in fact a trend toward a reversal of the normal pattern under verbal interference
such that cross-category trials are performed more slowly than within-category trials.
Although this is not a significant effect, it is consistent with the reversal in category
advantage under verbal interference reported in another work (23) and may suggest an
obligatory attempt to make a verbal distinction even when a dual task interferes with such
an attempt.
none spatial verbal
interference condition
cross category
within category
none spatial verbal
interference condition
cross category
within category
Fig. 2. Russian speakers’ (Left) and English speakers’ (Right) reaction times
(msec) shown for the no-interference, spatial-interference, and verbal-
interference conditions. Both near-color and far-color comparisons are in-
cluded in these graphs. Error bars represent one SE of the estimate of the
two-way interaction between category and interference condition.
no interference
spatial interference
verbal interference
near colors far colors
Russian speakers English speakers
near colors far colors
Fig. 3. Category advantage is plotted for Russian speakers (Left) and English
speakers (Right) as a function of comparison distance (near color vs. far color)
and interference condition (none, spatial, and verbal). Category advantage is
calculated as the difference between the average reaction time for within-
category trials and that for cross-category trials (msec). Error bars represent
one SE of the estimate of the three-way interaction among category, inter-
ference condition, and color distance.
www.pnas.orgcgidoi10.1073pnas.0701644104 Winawer et al.
(20) 0.24; P 0.81] nor bet ween any conditions in the far color
trials (P 0.78).
Unlike Russian speakers, English speakers did not show any
category advant age [F (1, 20) 0.150; P 0.703] nor any
category interference interaction [F (2, 40) 0.422; P 0.659]
(Fig. 2), as revealed by the same 2 3 2 ANOVA (category
interference distance) of the English speakers’ data. The only
sign ificant effect in this analysis was a main effect of interfer-
ence, such that English speakers were fastest with no interfer-
ence and slowest with verbal interference [1,113, 1,156, and 1,216
msec for no interference, spatial interference, and verbal inter-
ference, respectively; F (2, 40) 5.170; P 0.010].
The results of English speakers differed significantly from
those of Russian speakers. In near-c olor trials, the difference in
the category advant age between no interference and verbal
interference was significantly g reater for Russian than English
speakers [189 vs. 15 msec, respectively; t (40) 2.17; P 0.036].
Likewise, the difference in category advantage between spatial
interference and verbal interference was significantly greater for
Russian speakers than English speakers [173 vs. 14 msec, re-
spectively; t (40) 2.142; P 0.038]. No differences were
observed for similar comparisons on far color trials (P 0.6 for
both comparisons).
Because the performance of Russian speakers on average was
slower than that of English speakers, we considered the possi-
bilit y that the interesting difference between the two language
groups was not due to native language but to overall speed. If
linguistic effects on discrimination were only observed in harder
(or slower) tasks, it is possible that English speakers automati-
cally verbally c oded the light blue/dark blue distinction but were
too quick overall for the linguistic system to be able to influence
the decision process. To test this possibility, we conducted a
un ivariate ANOVA, using language (Russian vs. English) as a
fixed factor and mean reaction time as a covariate. The depen-
dent variable was a composite measure of the linguistic effect of
interest, the categor y advant age under the nonverbal-
interference conditions (the mean of the spatial- and the no-
interference conditions) minus the category advantage under
the verbal-interference condition. For the near-color trials only,
the language group was a significant predictor of the linguistic
ef fect of interest [F (1, 39) 4.181; P 0.048]. Mean reaction
time was not a significant covariate [F (1, 39) 0.349; P 0.558].
This analysis confirms that differences in overall speed between
the two language groups were not responsible for the cross-
linguistic differences of interest between the two language
Accuracy. Because the stimuli were present on the screen until
subjects responded, accuracy was high (96.5 2.1% and 95.7
3.2% for English and Russian speakers, respectively). Further
analyses of the accuracy data by language, interference type, and
ef fects of category confir med that the differences of interest
found in reaction times could not be attributed to speed/accuracy
tradeof fs. There was one unex pected result in the accuracy data,
however: For near colors, Russian speakers were more accurate
on within-category, compared with cross-category, trials under
the no-interference condition (93% vs. 87%, or a 6% category
advant age, that is, a category disadvantage), but not under other
interference conditions and not in far-color trials, leading to a
three-way interaction among category, interference, and dis-
t ance [F (2, 40) 4.106, P 0.024]. To test whether the pattern
of results found in reaction time resulted from a speed/accuracy
tradeof f, we c onducted two further analyses of the near-color
trials. Both analyses suggested that a speed/ac curacy tradeof f
c ould not explain our results. First, the category advantage in
ac curacy showed little difference between the spatial and verbal
interference blocks, and it in fact differed more for the English
speakers (2.4% vs. 1.8%, spatial vs. verbal interference) than
for the Russian speakers (1.9% vs. 0.5%). Second, there was
a significant partial c orrelation between language group (En-
glish vs. Russian, coded as 0 or 1) and a composite measure of
the reaction time effect (see Detailed Analyses above) when
c ontrolling for accuracy (using the same composite measure)
[Pearson’s r (39) 0.365; P 0.019]. The converse was not true:
there was not a c orrelation between language group and accu-
rac y when controlling for reaction time [r (39) 0.096; P
We found that Russian speakers were faster to discriminate two
c olors if they fell into different linguistic categories in Russian
(one siniy and the other goluboy) than if the two colors were
f rom the same category (both sin iy or both goluboy). This
category advantage was eliminated by a verbal, but not a spatial,
dual t ask. Further, effects of language were most pronounced on
more difficult, finer discriminations. English speakers tested on
the identical stimuli did not show a category advantage under any
c ondition. These results demonstrate that categories in language
can affect perfor mance of basic perceptual color discrimination
t asks. Further, they show that the effect of language is online,
because it is disrupted by verbal interference. Finally, they show
that color discrimination performance differs across language
groups as a function of what perceptual distinctions are habit-
ually made in a particular language.
The case of the Russian blues suggests that habitual or
obligatory categorical distinctions made in one’s language result
in language-specific categorical distortions in objective percep-
tual t asks.** English speakers, of course, also can subdivide blue
stimuli into light and dark. In fact, English speakers as a g roup
drew nearly the same boundary as did the Russian speakers in
our work. The critical difference in this case is not that English
speakers cannot distinguish between light and dark blues, but
rather that Russian speakers cannot avoid distinguishing them:
they must do so to speak Russian in a c onventional manner. This
c ommunicative requirement appears to cause Russian speakers
to habitually make use of this distinction even when performing
**This may apply to some, but not necessarily all, perceptual tasks. Evidence from other
studies with similar designs suggests that perceptual discriminations that are more
difficult (unpublished work) and ones that are carried out in the right visual field (and
therefore more strongly in the left hemisphere of the brain, typically associated with
language) (23) are more likely to be affected by linguistic processes.
Table 1. Mean reaction times in msec (and SEM) for all conditions
Russian speakers English speakers
Near-color Far-color Near-color Far-color
Interference Between Within Between Within Between Within Between Within
None 1,164 (66) 1,288 (77) 998 (55) 999 (55) 900 (51) 914 (52) 758 (36) 735 (32)
Spatial 1,162 (58) 1,270 (56) 1,096 (64) 1,096 (53) 911 (41) 922 (46) 819 (37) 835 (43)
Verbal 1,325 (55) 1,260 (50) 1,146 (62) 1,132 (50) 952 (41) 955 (46) 831 (41) 821 (35)
Winawer et al. PNAS
May 8, 2007
vol. 104
no. 19
a perceptual task that does not require language. The fact that
Russian speakers show a category advantage across this color
boundary (both under normal viewing conditions w ithout inter-
ference and despite spatial interference) suggests that language-
specific categorical represent ations are normally brought online
in perceptual decisions.
These results also help to clarify the mechanisms through
which linguistic categories can influence perceptual perfor-
mance. It appears that the influence of linguistic categories on
c olor judgments is not limited to tasks that involve remembering
c olors across a delay. In our task, subjects showed language-
c onsistent distortions in perceptual performance even though all
c olors were in plain view at the time of the perceptual decision.
Further, language-consistent distortions in color judgments were
not limited to ambiguous or subjective judgments where subjects
may explicitly adopt a language-consistent strateg y as a guess at
what the experimenter wants them to do (19). In our task,
subjects showed language-c onsistent distortions in perceptual
performance while making objective judgments in an unambig-
uous perceptual discrimination t ask with a clear, correct answer.
Results from the verbal interference man ipulation provide
further hints about the mechanism through which language
shapes perceptual performance in these tasks. One way that
language-specific distortions in perceptual performance could
arise would be if low-level visual processors tuned to some
particular discriminations showed long-term improvements in
precision, whereas processors tuned to other discriminations
bec ome less precise or remain unchanged (25). Very specific
improvements in perceptual perfor mance are widely observed in
perceptual learning literature and are often thought to reflect
changes in the synaptic connections in early sensory processing
areas (26). Our present results do not offer support for this
possibilit y because a simple task manipulation, ask ing subjects to
remember digit series, eliminated the language-specific distor-
tions in discrimination. If the language-specific distortions in
perceptual discrimination had been a product of a permanent
change in perceptual processors, temporarily disabling access to
linguistic representations with verbal interference should not
have changed the pattern in perceptual perfor mance.
Instead, our results suggest that language-specific distortions in
perceptual performance arise as a function of the interaction of
lower-level perceptual processing and higher-level knowledge sys-
tems (e.g., language) online, in the process of arriving at perceptual
decisions. The exact nature of this interaction cannot be determined
from these data. It could be that information from linguistic systems
directly influences the processing in primary perceptual areas
through feedback connections, or it could be that a later decision
mechanism combines inputs from these two processing streams. In
either case, it appears that language-specific categorical represen-
tations play an online role in simple perceptual tasks that one would
tend to think of as being primarily sensory. Language-specific
representations seem to be brought online spontaneously during
even rather simple perceptual discriminations. The result is that
speakers of different languages show different patterns in percep-
tual discrimination performance when tested under normal viewing
conditions. When normal acce ss to language-specific representa-
tions is disrupted (as under the verbal-interference condition),
language-specific distortions in discrimination performance also
These c onclusions are also consistent with three other findings
using similar methodologies. In one study, a verbal dual task was
shown to selectively interfere with blue/green discriminations
among English speakers using the same triad presentations used
here (21). In two studies a v isual field manipulation was used to
test the hypothesis that language effects are more pronounced in
the right visual hemifield (and hence the left, presumably
language-dominant, hemisphere) (22, 23). These studies (22, 23)
found that visual search time was affected more strongly by a
dual verbal task for cross-category searches in the right than the
lef t visual hemifield. In all four studies (the present work and
refs. 21–23), a category advantage was observed in simple
perceptual tasks and the category advantage was selectively
eliminated or reduced by verbal, but not spatial, interference.
Parallel findings using t wo very different manipulations, a
cross-linguistic comparison and a bet ween-hemispheres compar-
ison, converge to make a strong case that language-specific
processes can affect simple, implicit, perceptual decisions.
The Whorfian question is often interpreted as a question of
whether language af fects nonlinguistic processes. Putting the
question in this way presupposes that linguistic and nonlinguistic
processes are highly dissociated in normal human cognition, such
that many tasks are accomplished without the involvement of
language. A different approach to the Whorfian question would
be to ask the extent to which linguistic processes are normally
involved when people engage in all kinds of seemingly nonlin-
guistic tasks (e.g., simple perceptual discriminations that can be
ac complished in the absence of language). Our results suggest
that linguistic representations normally meddle in even surpris-
ingly simple objective perceptual decisions.
Participants. Twenty-six native Russian speakers (28.9 10.2
years old, mean SEM) and 24 native English speakers (26.3
9.2 years old) were recruited from the Boston area and tested at
the Massachusetts Institute of Technology (MIT) (Cambridge,
M A). The age of English acquisition for Russian speakers ranged
f rom 7 to 21 years. Participants gave written consent and were
paid for their time. The experimental protocol was approved by
the MIT Human Subjects Committee.
Materials and Design. Each subject completed one block of 136
c olor discrimination trials without any secondary task (‘‘no
interference’’), one block while performing a secondary verbal-
interference task, and one block while performing a control,
spatial-interference task. The order of the blocks was varied
randomly across subjects. After completing the color discrimi-
nation trials, subjects were tested in a separate c olor-naming t ask
to determine their individual linguistic borders. Subjects were
shown the 20 stimuli (twice each) in random order and asked to
classif y each color with a key press, either siniy vs. goluboy (for
Russian speakers) or dark blue vs. light blue (for English
Subjects were instructed to make all judgments as quickly and
ac curately as possible. All subjects received the same instruc-
tions in English. Testing took place in a quiet, darkened room.
Color Stimuli. Twenty computer-simulated color chips were cre-
ated for this study, ranging from goluboy or light blue to sin iy or
dark blue (Fig. 1). The Commission Internationale de l’Eclairage
(CIE) Yxy coordinates ranged from 84, 0.214, 0.255 (stimulus 1)
to 5.3, 0.154, 0.09 (stimulus 20). The stimuli differed primarily in
the luminance axis (Y) and the y chromaticit y axis, consistent
with reports on Russian color categorization (e.g., see ref. 1; for
review, see refs. 2 and 27). The color squares were 2.5 cm per
side, and subjects viewed the screen from 60 cm.
Color Discrimination Task. In each c olor discrimination trial, sub-
jects were shown a triad of color squares. One of the colors
presented on the bottom was physically identical to the top color
square (Fig. 1). The task was to indicate which of the bottom
squares matched the top square by pressing a key on the right or
lef t side of the keyboard. The nonmatching/distracter color
square was either very similar to the other two (two steps apart
in our continuum of 20, a near-color c omparison) or more
dif ferent (four steps apart, a far-color comparison).
www.pnas.orgcgidoi10.1073pnas.0701644104 Winawer et al.
The Interference Conditions. No-interference blocks consisted of
only color discrimination trials as described above. In verbal-
interference blocks, subjects were given an eight-digit number
series to rehearse during the color task. This series was presented
for 3 sec, and subjects were instr ucted to rehearse it silently.
Subjects rehearsed the number series while completing eight
c olor discrimination trials; their recall was then tested by choos-
ing between the original series and a foil which differed by
one digit.
In spatial-interference blocks, subjects v iewed a 4 4 square
grid of which four random squares were shaded black. Subjects
were instructed to remember the grid pattern by maintaining a
picture of it in their mind until tested. As with the verbal-
interference condition, a two-choice test was given after eight
intervening color discrimination trials. The incorrect grid dif-
fered in the location of one shaded square.
The spatial- and verbal-interference tasks were pretested for
dif ficulty in the absence of a primary task and found to result in
equal ac curacy (g rids, 95 1% c orrect; numbers, 96% 1%
c orrect; two-tailed t (10) 0.94; P 0.35). Each of the three
blocks consisted of 136 color trials, w ith 17 interference stimuli
used in each of the two interference blocks. Each color appeared
equally often on the left and right and equally often as the match
and the distracter.
We thank the citizens of Cognation for insightful comments and
discussions. This research was funded by National Science Foundation
CAREER Grant 0608514 (to L.B.).
1. Dav ies IR, Corbett GG, McGurk H, MacDermid C (1998) J Child Lang
2. Paramei GV (2005) Cross Cult Res 39:10–38.
3. Berlin B, Kay P (1969) Basic Color Terms: Their Universality and Evolution
(Univ of Californ ia Press, Berkeley, CA).
4. Roberson D, Davidoff J, Davies IR, Shapiro LR (2005) Cognit Psychol
5. Roberson D, Davies I, Davidoff J (2000) J Exp Psychol Gen 129:369–398.
6. Heider ER (1972) J Exp Psychol 93:10–20.
7. Lindsey DT, Brown AM (2002) Psychol Sci 13:506–512.
8. Heider ER, Oliver DC (1972) Cognit Psychol 3:337–354.
9. Lucy JA, Schweder RA (1979) Am Anthropol 81:581–615.
10. Kay P, Kempton W (1984) Am Anthropol 86:65–79.
11. Brown R (1976) Cognition 4:125–153.
12. Pilling M, Davies IR (2004) Br J Psychol 95:429455.
13. Pilling M, Wiggett A, Ozgen E, Dav ies IR (2003) Mem Cognit 31:538–551.
14. Davies IR, Corbett GG (1997) Br J Psychol 88:493–517.
15. Whorf BL (1956) Language, Thought, and Reality: Selected Writings (Technol
Press of MIT, Cambridge, MA).
16. Dav ies IR, Sowden PT, Jerrett DT, Jerrett T, Corbett GG (1998) Br J Psychol
17. Roberson D, Davidoff J, Davies IR, Shapiro LR (2004) J Exp Psychol Gen
18. Roberson D, Davidoff J (2000) Mem Cognit 28:977–986.
19. Pinker S (1994) The Language Instinct (Morrow, New York).
20. Munnich E, Landau B (2003) in Language in Mind: Advances in the Study of
Language and Thought, eds Gentner D, Goldin-Meadow S (MIT Press,
Cambridge, MA), pp 113–157.
21. Witthoft N, Winawer J, Wu L, Frank M, Wade A, Boroditsky L (2003) in
Proceedings of the 25th Annual Meeting of the Cognitive Science Society, eds
Alterman R, Kirsh D (Lawrence Erlbaum, Mahwah, NJ).
22. Drivonikou GV, Kay P, Regier T, Ivry RB, Gilbert AL, Franklin A, Davies IRL
(2007) Proc Natl Acad Sci USA 104:1097–1102.
23. Gilbert A L, Regier T, Kay P, Ivry RB (2006) P roc Natl Acad Sci USA
24. Bornstein MH, Korda NO (1984) Psychol Res 46:207–222.
25. Goldstone R (1994) J Exp Psychol Gen 123:178–200.
26. Karni A (1996) Brain Res Cognit Brain Res 5:3948.
27. Moss A, Davies I, Corbett G, Laws G (1990) Lingua 82:313–332.
Winawer et al. PNAS
May 8, 2007
vol. 104
no. 19


Remember, *"within category”* means that the 2 bottom squares (the match and the distracter) were both dark blue (“siniy”) or light blue (“goluboy”). *"Cross category”* means that one square was a dark blue and the other square was a light blue. You would expect that native Russian speakers would be faster to spot the correct square when the distracter square is a different color than the match one. As the graph indicates, you do see a category advantage (i.e. shorter reaction times) for *cross category* cases. This advantage goes away with verbal interference. In the cited studies they use the same verbal interference method used in this paper, namely, silently rehearsing a number while performing another task. Cited studies: - [21] [Effects of language on color discriminability]( - [22] [Further evidence that Whorfian effects are stronger in the right visual field than the left]( - [23] [Whorf hypothesis is supported in the right visual field but not the left]( #### ANOVA ANOVA stands for *Analysis of Variance*. It is a collection of statistical models used for analyzing the results of an experiment with multiple groups. ANOVA was pioneered by statistician and evolutionary biologist Ronald Fisher. The observed variance in a variable is partitioned into components attributable to the different sources of variation. A Mixed 2X2 ANOVA compares the mean differences between two groups that have been split on two factors. One factor is a within-subjects factor and the other is a between-subjects factor. If you want to learn more about this topic I would suggest [Chapter 11 of "Experimental Design and Analysis" by Howard Seltman]( There has been a long standing debate surrounding the concept of linguistic relativity, which ponders whether language influences thought, and if so, how / how much. The *Sapir-Whorf hypothesis* (also known as *Whorfianism* / *Whorfian Hypothesis*) suggests that language indeed determines (or at least influences) a native speaker’s perception and categorization of experiences. The color debate is an important part of this larger discussion. There are plenty of interesting differences between languages when it comes to how they describe colors. For instance, before the modern period, Japanese had just one word, *Ao*, for both blue and green. In fact, in Japan people often refer to the “go” traffic light as being blue in color even tho it is just as green as anywhere else in the world. In Thai *khiaw* refers to green except when you are talking about the sea or the sky in which case it is blue. Sometimes these differences between languages are subtle, but sometimes they can be quite extreme. For instance to the *Dani* people of the highlands of New Guinea objects come in two shades: - *mili* for darker/cooler shades (blue, green, black) - *mola* for lighter/warmer shades (red, yellow, white) In general, European languages divide the spectrum like pictured below: ![color spectrum]( As stated tho, Russian distinguishes between light blues (“goluboy”) and dark blues (“siniy”). There is no single word that encompasses “goluboy” and “siniy”. These are 2 separate colors in their own right. ![Russian Blues](