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
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
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
the colors in Fig. 1. Unlike English, Russian makes an obligatory
distinction between lighter blues (‘‘goluboy’’) and darker blues
(‘‘siniy’’). Like other basic color words, ‘‘siniy’’ 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 color confusability in memory (4, 5, 10, 12, 16). For
example, if two colors 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
compared with people whose language assigns different names
the acquisition of color terms (17). Further, cross-linguistic
differences in similarity judgments and recognition memory can
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-
ilarity ratings has left some critics unconvinced (19, 20). Pinker
(19) summarizes the critiques as follows:
ifferent languages divide color space differently. For exam-
ple, the English term ‘‘blue’’ can be used to describe all of
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. . . . All [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 color chips go together;
they often put the ones together that have the same
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
pretty similar. Well, I’d probably call these two ‘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 color discrimination perfor-
mance has remained. Studies testing only color 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-congruent
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 color discrimination performance 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
color 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
minimal 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 accuracy. This feature of the design addressed
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: firstname.lastname@example.org.
© 2007 by The National Academy of Sciences of the USA
May 8, 2007 ?
vol. 104 ?
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.
Second, all stimuli involved in a perceptual decision (in this
case, the three color squares) were present on the screen
simultaneously 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
explicitly. 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 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? Previ-
ous studies with English speakers have demonstrated that verbal
interference changes English speakers’ performance in speeded
color discrimination (21) and in visual searching (22, 23) across
the English blue/green boundary. If a color boundary is present
in one language but not another, will the two language groups
differ 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
color discrimination task across a color boundary that exists in
Russian but not in English. Twenty color stimuli spanning 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 accurately as possible which of the two 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 effects 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 advantage.
For English speakers, it should not matter whether colors fall
into the same or different linguistic categories in Russian, so
they should not show any such differences.
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/siniy category
advantage found in Russian speakers. To evaluate this possibil-
ity, subjects performed the color discrimination task under three
there was no dual task; a verbal-interference condition, in which
subjects silently rehearsed digit strings while simultaneously
completing 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 condition was used to examine
whether any differences between the baseline condition and
verbal-interference condition 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 categories are more likely to play a role in perceptual tasks
To explore this finding with a new set of color stimuli and speakers
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 color classification task at the end of the experiment
(after 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
siniy 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/siniy 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
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
nine 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
Summary of Results. Russian speakers showed a category advan-
tage 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
condition. 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-color 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 (between 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 taking part in experi-
ments. The mean and SE for each condition are included in
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
condition: 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-color 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-color vs. far-color comparisons, for example), is consistent
a category advantage was observed for harder, but not (unpub-
lished work) easier, discriminations. There were no other sig-
nificant main effects or interactions in this analysis.
To explore in more detail the interaction among distance,
category, and interference, several planned t tests were con-
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 advantage 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
(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.
Russian speakers’ (Left) and English speakers’ (Right) reaction times
near colors far colors
Russian speakersEnglish speakers
near colors far colors
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.org?cgi?doi?10.1073?pnas.0701644104Winawer et al.
trials (P ? 0.78).
Unlike Russian speakers, English speakers did not show any
category advantage [F (1, 20) ? 0.150; P ? 0.703] nor any
(Fig. 2), as revealed by the same 2 ? 3 ? 2 ANOVA (category ?
interference ? distance) of the English speakers’ data. The only
significant effect in this analysis was a main effect of interfer-
ence, such that English speakers were fastest with no interfer-
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-color trials, the difference in
the category advantage between no interference and verbal
interference was significantly greater 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
Because the performance of Russian speakers on average was
slower than that of English speakers, we considered the possi-
bility 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 coded 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
univariate 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 category advantage 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
effect of interest [F (1, 39) ? 4.181; P ? 0.048]. Mean reaction
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
effects of category confirmed that the differences of interest
tradeoffs. There was one unexpected 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
advantage, 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-
tance [F (2, 40) ? 4.106, P ? 0.024]. To test whether the pattern
of results found in reaction time resulted from a speed/accuracy
tradeoff, we conducted two further analyses of the near-color
trials. Both analyses suggested that a speed/accuracy tradeoff
could not explain our results. First, the category advantage in
accuracy 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 correlation 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
controlling 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 correlation between language group and accu-
racy when controlling for reaction time [r (39) ? 0.096; P ?
We found that Russian speakers were faster to discriminate two
colors if they fell into different linguistic categories in Russian
(one siniy and the other goluboy) than if the two colors were
from the same category (both siniy or both goluboy). This
category advantage was eliminated by a verbal, but not a spatial,
dual task. Further, effects of language were most pronounced on
more difficult, finer discriminations. English speakers tested on
condition. These results demonstrate that categories in language
can affect performance of basic perceptual color discrimination
tasks. 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 tasks.** English speakers, of course, also can subdivide blue
stimuli into light and dark. In fact, English speakers as a group
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 conventional manner. This
communicative 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 speakersEnglish speakers
Interference Between Within BetweenWithin Between WithinBetweenWithin
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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 without inter-
ference and despite spatial interference) suggests that language-
specific categorical representations 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
color judgments is not limited to tasks that involve remembering
colors across a delay. In our task, subjects showed language-
consistent distortions in perceptual performance even though all
colors 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 strategy as a guess at
what the experimenter wants them to do (19). In our task,
subjects showed language-consistent distortions in perceptual
performance while making objective judgments in an unambig-
uous perceptual discrimination task with a clear, correct answer.
Results from the verbal interference manipulation 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
become less precise or remain unchanged (25). Very specific
improvements in perceptual performance 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
possibility because a simple task manipulation, asking 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 performance.
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
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-
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-
conditions. When normal access to language-specific representa-
tions is disrupted (as under the verbal-interference condition),
language-specific distortions in discrimination performance also
These conclusions 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 visual 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
left 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 two very different manipulations, a
cross-linguistic comparison and a between-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 affects nonlinguistic processes. Putting the
question in this way presupposes that linguistic and nonlinguistic
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
accomplished 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,
from 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
color 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-
to determine their individual linguistic borders. Subjects were
shown the 20 stimuli (twice each) in random order and asked to
classify 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
accurately 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 siniy 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 chromaticity 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 color 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
left 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 comparison) or more
different (four steps apart, a far-color comparison).
www.pnas.org?cgi?doi?10.1073?pnas.0701644104Winawer et al.
The Interference Conditions. No-interference blocks consisted of Download full-text
only color discrimination trials as described above. In verbal-
interference blocks, subjects were given an eight-digit number
for 3 sec, and subjects were instructed to rehearse it silently.
Subjects rehearsed the number series while completing eight
color discrimination trials; their recall was then tested by choos-
ing between the original series and a foil which differed by
In spatial-interference blocks, subjects viewed 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
difficulty in the absence of a primary task and found to result in
equal accuracy (grids, 95 ? 1% correct; numbers, 96% ? 1%
correct; two-tailed t (10) ? 0.94; P ? 0.35). Each of the three
blocks consisted of 136 color trials, with 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.).
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