Semantic Context Effects on Color Categorization

Posted: 13 Agustus 2010 in colour language

Semantic Context Effects on Color Categorization
Rony Kubat (
MIT Media Lab, 20 Ames Street
Cambridge, MA 02139 USA
and Daniel Mirman (
Moss Rehabilitation Research Institute, Albert Einstein Healthcare Network
1200 W. Tabor Rd.
Philadelphia, PA 19141
and Deb Roy (
MIT Media Lab, 20 Ames Street
Cambridge, MA 02139 USA
A number of recent theories of semantic representation propose
two-way interaction of semantic and perceptual information.
These theories are supported by a growing body of experiments
that show widespread interactivity of semantic and
perceptual levels of processing. In the current experiment, participants
classified ambiguous colors into one of two color categories.
The ambiguous colors were presented either as a color
patch (no semantic context), as an icon representing an object
that is strongly associated with one of the color options, or as a
word referring to such an object. Although the iconic and lexical
contexts were incidental and irrelevant to the color categorization
task, participants’ responses were consistently biased
toward the context color. These results extend previous findings
by showing that lexical contexts, as well as iconic contexts
influence color categorization.
Keywords: Color perception; Semantics; Interactive processing;
A number of current theories of the representation of word
meanings propose that these representations are composed of
perceptual and motor representations relevant for the word
meaning. Such theories may be called grounded because they
“ground” concepts in sensory/perceptual-motor/action representations
(Barsalou, Simmons, Barbey, & Wilson, 2003;
Roy, 2005); they may also be called distributed (McClelland
& Rogers, 2003) because they propose that semantic knowledge
is distributed throughout the perceptual-motor systems.
Under such views, semantic knowledge of, for example, the
meaning of color names involves precisely the same brain
regions and representations as perception of color. In other
words, the mechanisms of color perception are an intrinsic
part of semantic knowledge. Conversely, this view also suggests
that semantic knowledge is an intrinsic part of color perception.
This latter point is consistent with interactive (McClelland,
1993) and Bayesian (Geisler & Diehl, 2003) theories of perception
and cognition, which propose that top-down information
(such as semantic knowledge) directly influences lowerlevel
perceptual processes. Visual perception, in particular, is
a highly interactive process. The perception of illusory contours
is guided by top-down expectations (Lee & Nguyen,
2001) and recognition of objects is influenced by scene contexts
(Bar, 2004). Similarly, color perception appears to be
influenced by memory of an object’s color: when participants
were asked to adjust the color of a fruit object until it appeared
achromatic, their judgments were consistently shifted
away from gray in a direction opposite to the typical color
of the fruit (Hansen, Olkkonen, Sebastian, & Gegenfurtner,
2006). That is, the participants’ perception of the color of
a displayed fruit was biased by the conceptual knowledge of
the typical color of that fruit.
It has long been known that color perception is not merely
a matter of the wavelengths of light hitting the retina—if it
were, changes in illumination would radically alter the perceived
colors in the environment. There is now direct evidence
that top-down knowledge is used to improve color
constancy (Mitterer & de Ruiter, 2008), much like context
and word knowledge can be used to adjust speech sound categories
(Norris, McClelland, & Cutler, 2003; Mirman, Mc-
Clelland, & Holt, 2006). The use of context-dependent expectations
gives the perceptual system robustness in the face
of changing environments, which is a central principle of interactive
(McClelland, 1993) and Bayesian (Geisler & Diehl,
2003) theories.
The paradigm for studying the effects of conceptual knowledge
on color perception presented here is closely related to
the Stroop (1935) paradigm. In the Stroop paradigm, two
sources of information (traditionally, printed word and ink
color) may be congruent or conflicting, and the interference
caused by conflicting information reveals the interactions between
processing of these different sources of information.
In other words, the traditional Stroop paradigm tests the interaction
of conceptual information from lexical context and
perceptual information from color perception. The present
experiment uses a Stroop-like paradigm to test the interaction
of conceptual information (i.e., iconic or lexical contexts)
with color information. Thus, the critical principles of information
integration that are at work in the Stroop paradigm are
the same as those at work in the present experiment.
We extend previous studies of the effect of context on
color perception by testing whether in addition to iconic context,
lexical context influences perception of ambiguous colors
(i.e., ones that fall half-way between two basic color categories
(Berlin & Kay, 1969)). If color perception is part of
an interactive and/or a single integrated perceptual-cognitive
system, simple color categorization will be influenced by high
Figure 1: Screen captures from the experiment. From left to right: control, lexical context, iconic context. Colors in this figure
are not representative of the experimental stimulus due to variations in the display or printing of the figure.
level semantic contexts. In effect, an ambiguous brownpurple
patch will appear more brown when in the semantic
context of “chocolate”, and the same color will appear
more purple in the context of “eggplant.” By using iconic
and lexical contexts, this experiment engages abstract, highlevel
semantic representations that are significantly removed
from the low-level perceptual processes involved in color perception.
Although both iconic and lexical contexts activate
semantic representations, how those representations are activated
is somewhat different: iconic contexts activate representations
though object recognition, more directly than lexical
contexts, which activate through language. Testing both
iconic and lexical contexts within the same experiment makes
the results directly comparable. An influence of these semantic
contexts on color classification would demonstrate comprehensive
interactivity of the perceptual-semantic system for
the color domain.
Across eight different pairs of basic color categories, Englishspeaking
subjects chose which of two basic color terms better
described an ambiguous color. Two different semantic context
variations were tested. In the lexical-context condition
a word with strong single color association (for example, the
word “chocolate” for brown, and “eggplant” for purple) was
colored in the ambiguous color. Similarly, in the iconic context
condition a high contrast icon of an object commonly associated
with a particular color category was presented in the
ambiguous color. For the control condition, the ambiguous
color was presented as a square swatch. If semantic context
influences color perception, then participants should be more
likely to label the ambiguous color consistent with the lexical
or iconic context. That is, an ambiguous color between brown
and purple (for example) should be labeled “brown” more often
in the icon or word context of “chocolate” and labeled
“purple” in the icon or word context of “eggplant”. Figure 1
shows example displays for each condition. A more detailed
exposition of this experiment can be found in (Kubat, 2008).
Ambiguous Color Calibration
A calibration pilot study was conducted to identify the ambiguous
colors. For each color pair in Table 1, a rectangular
swatch was presented flanked by the two basic color terms.
Below the swatch and color labels, a slider allowed participants
to change the mix between the two colors that filled the
center swatch. Participants were instructed to use the slider
to find the color that is perfectly ambiguous between the two
color terms.
CIELab values for the focal colors i and j were chosen
from the World Color Survey (Kay, Berlin, Maffi, & Merrifield,
2003) stimulus palette, clamped to the sRGB color
space. The CIELab value of the center (ambiguous) color
swatch was determined as:
L =
iL + jL
, a =(1−a)ia+aja , b =(1−a)ib+ajb
where a is the slider value, ranging over [0, 1]. The luminance
of the ambiguous color was fixed in order to minimize
biasing based on perceived brightness and minimize perceptual
contrast effects due to the experimental stimuli being presented
against a neutral gray background.
Five ambiguous colors were later generated for each
color pair using the alpha values of μi j + bsi j, with b 2
{−1.5,−0.75,0,0.75,1.5}, where μi j and si j are the mean
and standard deviation of slider values chosen by the participants
for color pair hi, ji. By choosing these five deviations,
the experiment control case is forced toward 50%, making
any context effects discernible despite individual variations
in color category boundaries.
Thirteen participants from the MIT community completed
the calibration pilot study.
Twenty-three participants from the MIT community completed
the main experiment (mean age = 37.5; 8 male, 15
female). All subjects were proficient speakers of English and
reported normal color vision. None had participated in the
calibration pilot study.
Materials and Procedure
The experiment was performed in a windowless, dimly lit
room illuminated at approximately 3200K. Participants
were given time to adjust to the ambient lighting in the room
before any color-related tasks were performed. Stimuli were
presented with custom-written software on an Apple Macintosh
computer and data recorded to a relational database. A
Color Pair Semantic Context
hA, Bi A lexical context B lexical context A iconic context B iconic context
Red Green “Cherry” “Broccoli” Cherry Leaf
Green Blue “Broccoli” “Ocean” Leaf Waves
Blue Red “Ocean” “Cherry” Waves Cherry
Pink Yellow “Flamingo” “Schoolbus” Flamingo Corn
Yellow Orange “Schoolbus” “Carrot” Corn Traffic Cone
Orange Purple “Carrot” “Eggplant” Traffic Cone Grapes
Purple Brown “Eggplant” “Chocolate” Grapes Log
Brown Pink “Chocolate” “Flamingo” Log Flamingo
Table 1: Color pairs used in the experiment and the lexical and iconic contexts used. Images of the icons used are shown in
Figure 2.
Figure 2: Icons used in the experiment.
30-inch Apple Cinema Display was used as the display device.
The monitor was calibrated to the sRGB standard (D65,
2.2 Gamma) using a ColorVision Spyder2Pro hardware color
calibrator. All color stimuli were presented against a neutral
gray background. The equipment for the ambiguous color
calibration was identical to the experimental equipment.
In the main experiment, participants were presented with a
color stimulus in the center of the screen, and two color labels
in black text on the left and right. In order to proceed to
the next screen, each participant chose which of the two labels
better represented the center stimulus by pressing a key
on the keyboard. Participants were instructed to proceed as
quickly as they believed they could make an effective decision.
To minimize color saturation effects, the stimulus color
was only visible for 1500 milliseconds and was preceded with
one second of neutral gray. Response time was recorded.
Ten trials were conducted for each condition (A lexical
context, B lexical context, A iconic context, B iconic context,
control) of each color pair hA,Bi in Table 1. The ten
trials presented the five ambiguous colors for each color pair
in both color label orientations (left–right: A–B and B–A).
Trials across all color pairs were divided into three blocks
(control, lexical context, and iconic context) and randomized
within block.
For the lexical context task, eight nouns were chosen based
on their strong associations with a basic color category. Similarly,
eight icons were selected to provide context—each with
a strong color association. Table 1 details the words and icons
used; Figure 2 shows the icons. This brought the total number
of samples for each color pair to ten for the context-free
(control) case, and twenty otherwise. The total number of
decisions per participant collected during the experiment was
400 (8 color pairs, 10 control trials, 20 icon-context trials, and
20 work-context trials for each color pair).
The mean of participants’ responses across color pairs, for
each type of context, are shown in Figure 3. If a particular
color continuum was not perceived as ambiguous by a particular
participant (i.e., same color category response for all 10
control trials), that continuum was excluded from analysis for
that participant. In the control condition, where the ambiguous
color was presented as a square swatch, participants were
about equally likely to choose each of the two responses (A
response: mean = 52.7%, SEM = 1.9%). This slight bias
Iconic context Lexical context
Proportion “A”
“A” Context
“B” Context
Figure 3: Mean “A” response for each type of context (error
bars indicate 1SEM). Control (no context) condition response
is illustrated by the solid line (dashed lines reflect 1SEM).
Context biased color naming responses, causing an increase
in context-consistent color naming for both lexical and iconic
Color Pair Percent Congruent
hA,Bi Iconic context Lexical context n
Red Green 33.42% 37.37% 19
Green Blue 44.05% 47.38% 21
Blue Red 58.18% 60.00% 22
Pink Yellow 44.13% 45.87% 23
Yellow Orange 62.83% 64.35% 23
Orange Purple 63.53% 67.94% 17
Purple Brown 60.87% 60.87% 23
Brown Pink 63.61% 62.78% 18
Table 2: Percentage of participants who made a congruent
choice (choosing A in an A context, or choosing B in a B
context) separated by color pair. The number of included
participants (those who found the control condition at least
somewhat ambiguous) is n.
away from 50% reflects that though colors were very ambiguous,
they were not perfectly ambiguous for every participant.
Critically, the colors were sufficiently ambiguous to
allow the predicted context effects to emerge. Iconic contexts
had a reliable effect on responses (t(22) = 2.90, p < 0.01):
when the ambiguous color was presented in the context of
an A-biased icon participants were more likely to respond A
(mean = 57.6%, SEM = 2.6%) and less likely to respond
A in the context of a B-biased icon (mean = 50.2%, SEM
= 2.7%). Lexical contexts also had a reliable effect on responses
(t(22) = 3.05, p < 0.01). When the ambiguous color
was presented in the context of an A-biased word, participants
were more likely to respond A (mean =60.5%, SEM =3.0%)
and less likely to respond A in the context of a B-biased word
(mean = 49.7%, SEM = 2.7%). Table 2 separates the results
by color pair. Five of the eight color continua showed strong
context effects: greater than 50
Mean response times to the two context tasks were statistically
identical to the control task indicating that there was
no noteworthy additional cognitive loading added by the context.
Response times separated between context-consistent
and context-inconsistent responses (Figure 4) show a Strooplike
effect for iconic context (congruent: mean = 1539.51
ms, SEM = 82.43; incongruent: mean = 1632.92 ms, SEM
= 93.91; t(22) = 2.74, p < 0.05), and an analogous though
non-significant trend for lexical context (congruent: mean
= 1640.87 ms, SEM = 99.16; incongruent: mean = 1680.72
ms, SEM = 95.36, t(22) = 1.47, p = 0.155). Response times
for the control condition had mean 1600.54 ms, SEM =
91.20. Note that in a traditional Stroop paradigm congruency
between color and lexical context are manipulated, whereas
this analysis compares identical displays in which the participants
produced congruent vs. incongruent responses. Incongruent
responses were slower (statistically reliable only for
iconic contexts), suggesting that they were produced in the
face of competition from context information.
These results demonstrate a semantic effect on color categorization.
When ambiguous colors were presented in the context
of a word or iconic image, categorization of the colors
was biased in the direction of the typical color of that pictured
or named object. That is, participants’ semantic knowledge
of the typical color of common objects shifted their perception
of ambiguous colors to be closer to the typical color for
that object. The knowledge that chocolate is typically brown
shifted the perception of its color to be more brown. This result
replicates previous findings of semantic effects on color
perception (Hansen et al., 2006; Mitterer & de Ruiter, 2008),
and extends them by showing that both iconic and lexical semantic
contexts influence color categorization.
There are three interpretations of the observed phenomena:
(1) context effects are a decision-level, feed-forward effect,
(2) they are a result of top-down interaction effects, or (3)
they due to shared representation of color between perceptual
and conceptual processing.
Under the assumption of separate cognitive components,
one dedicated to color perception and another dedicated to
conceptual processing, the flow of information distinguishes
between strictly feed-forward and interactive views of visual
processing. In the feed-forward view, information flow is unidirectional
from perception to conceptual processing. In the
present experiment participants categorized ambiguous colors
by choosing which of two color labels was a better label
for the stimulus color. This label-based color categorization
task requires access to semantic knowledge about the colors
denoted by the labels. As a result, the task can be re-cast
as semantic categorization—similar to semantic categorization
experiments where participants categorize objects as “animals”
or “plants”; in this case participants chose between
“yellow” and “pink” as semantic categories. Under this view,
one could argue that top-down feedback is not necessary to
account for the results since all of the critical processing took
place at the semantic level. Necessarily, any contextual ef-
1000 Control Iconic Context Lexical Context
Response time (ms)
Congruent Responses
Incongruent Responses
Figure 4: Mean response times for control, congruent and
incongruent responses for both iconic and lexical contexts in
forced choice tasks (error bars indicate 1SEM).
fects from icons or words take place at a post-perceptual decision
level that integrates perceptual and conceptual information.
This account is problematic with respect to recent
results (Mitterer & de Ruiter, 2008) which show recalibration
of color categories that could not be produced by decisionlevel
integration of color and conceptual knowledge.
The interactive interpretation allows information to flow
between components bidirectionally, with conceptual processing
directly influencing perceptual processing; namely,
by biasing the perception of color toward the context concept.
The findings presented here are consistent with a long history
of evidence of top-down effects in visual processing (Bar,
2004), as well as other domains (McClelland, 1993).
According to distributed and perceptually-grounded theories
of semantic representation (Barsalou et al., 2003; Mc-
Clelland & Rogers, 2003) semantic representations are based
on the relevant perceptual representations. That is, the semantic
representations of “yellow” and “pink” are, in large part,
the perceptual representations of yellow and pink colors. The
modules are not distinct entities, but rather intimately intertwined,
sharing state. This view represents an extension of
the interactive view in which bidirectional information flow
blurs the distinction between components. Under the distributed/
grounded view, conceptual effects on color perception
are due to integration of conceptual and perceptual representations,
rather than top-down feedback between distinct
representational levels.
Whether one takes the interactive or integrated view, the
present results add to the growing body of evidence that the
perceptual-semantic system does not consist of isolated modular
components. If just seeing the word chocolate or a simple
icon representing it is enough to influence the perception of
an ambiguous color, then the very notion of a strictly bottomup
color perception module that is independent of the perceiver’s
knowledge and context is undermined. If there are
processing components, they are richly interactive. Alternatively,
it may be more appropriate to consider the perceptualsemantic
system as an integrated whole with no truly independent
The authors would like to thank Matthew Ng and the members
of Cogmac for their comments and suggestions. This
paper is based upon work supported under a National Science
Foundation Graduate Research Fellowship to RK and
by National Institutes of Health grant F32HD052364 to DM.
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