What Color Is Your Anger? Assessing Color-Emotion Pairings in English Speakers

Abstract

Do English-speakers think about anger as “red” and sadness as “blue”? Some theories of emotion suggests that color(s)—like other biologically-derived signals- should be reliably paired with an emotion, and that colors should differentiate across emotions. We assessed consistency and specificity for color-emotion pairings among English-speaking adults. In study 1, participants (n = 73) completed an online survey in which they could select up to three colors from 23 colored swatches (varying hue, saturation, and light) for each of ten emotion words. In study 2, different participants (n = 52) completed a similar online survey except that we added additional emotions and colors (which better sampled color space). Participants in both studies indicated the strength of the relationship between a selected color(s) and the emotion. In study 1, four of the ten emotions showed consistency, and about one-third of the colors showed specificity, yet agreement was low-to-moderate among raters even in these cases. When we resampled our data, however, none of these effects were likely to replicate with statistical confidence. In study 2, only two of 20 emotions showed consistency, and three colors showed specificity. As with the first study, no color-emotion pairings were both specific and consistent. In addition, in study 2, we found that saturation and lightness, and to a lesser extent hue, predicted color-emotion agreement rather than perceived color. The results suggest that previous studies which report emotion-color pairings are likely best thought of experiment-specific. The results are discussed with respect to constructionist theories of emotion.

Full text

ORIGINAL RESEARCH
published: 26 February 2019
doi: 10.3389/fpsyg.2019.00206
Frontiers in Psychology | www.frontiersin.org 1February 2019 | Volume 10 | Article 206
Edited by:
Anna M. Borghi,
Sapienza University of Rome, Italy
Reviewed by:
Adrian Von Muhlenen,
University of Warwick,
United Kingdom
Lucia Riggio,
University of Parma, Italy
*Correspondence:
Jennifer Marie Binzak Fugate
jfugate@umassd.edu
Specialty section:
This article was submitted to
Cognition,
a section of the journal
Frontiers in Psychology
Received: 13 August 2018
Accepted: 21 January 2019
Published: 26 February 2019
Citation:
Fugate JMB and Franco CL (2019)
What Color Is Your Anger? Assessing
Color-Emotion Pairings in English
Speakers. Front. Psychol. 10:206.
doi: 10.3389/fpsyg.2019.00206
What Color Is Your Anger? Assessing
Color-Emotion Pairings in English
Speakers
Jennifer Marie Binzak Fugate*and Courtny L. Franco
Psychology, University of Massachusetts Dartmouth, Dartmouth, MA, United States
Do English-speakers think about anger as “red” and sadness as “blue”? Some theories
of emotion suggests that color(s)—like other biologically-derived signals- should be
reliably paired with an emotion, and that colors should differentiate across emotions. We
assessed consistency and specificity for color-emotion pairings among English-speaking
adults. In study 1, participants (n=73) completed an online survey in which they could
select up to three colors from 23 colored swatches (varying hue, saturation, and light)
for each of ten emotion words. In study 2, different participants (n=52) completed
a similar online survey except that we added additional emotions and colors (which
better sampled color space). Participants in both studies indicated the strength of the
relationship between a selected color(s) and the emotion. In study 1, four of the ten
emotions showed consistency, and about one-third of the colors showed specificity, yet
agreement was low-to-moderate among raters even in these cases. When we resampled
our data, however, none of these effects were likely to replicate with statistical confidence.
In study 2, only two of 20 emotions showed consistency, and three colors showed
specificity. As with the first study, no color-emotion pairings were both specific and
consistent. In addition, in study 2, we found that saturation and lightness, and to a lesser
extent hue, predicted color-emotion agreement rather than perceived color. The results
suggest that previous studies which report emotion-color pairings are likely best thought
of experiment-specific. The results are discussed with respect to constructionist theories
of emotion.
Keywords: color, emotion, color-emotion pairings, theories of emotion, data resampling
INTRODUCTION
The 2015 Pixar movie Inside Out is about a girl who has five “basic” emotions living in her head.
Each one is colored uniquely (e.g., anger is “red”, fear is “purple”, and disgust is “green”). The
idea represented in the movie is that color—just like a set of behaviors, facial expressions, and/or
vocalizations—distinguishes one emotion from another. The idea that emotions are real entities,
fixed in nature, isn’t just a Hollywood invention, however. The idea that emotions are fixed entities
with biological “blueprints” has prevailed in Psychology for nearly 150 years (see Gendron and
Barrett, 2009 for a history of emotion theory). Despite growing evidence that suggests emotions
might be better conceptualized as nominal rather than natural kinds (Barrett, 2006a,b, 2011, 2012,
2017; Barrett et al., 2007), this idea has not faded quietly. In fact, emotions are so engrained in our
world and schemas, nearly every language expresses them as if they were fixed entities. In addition,

Fugate and Franco Emotion and Color
people have strong emotional associations with colors, and even
young children have strong color preferences and have specific
emotional characteristics that they ascribe to colors (Boyatzis and
Varghese, 1994; Zentner, 2001). Certainly such color-emotion
pairings fit our folk psychology, as is expressed in our linguistic
metaphors. In English, we talk about “feeling blue,” being “green
with envy,” or “turning red in the face.” But do these color-
emotion pairings reflect the structure of emotions themselves?
Two different emotion theories (reviewed next) make two very
different predictions.
This paper seeks to address whether emotions are specifically
and consistently identified with a color within English-speaking
samples. Specificity refers to the fact that one (or more) colors
distinguish an emotion from another (e.g., a color is specific to
an emotion). Consistency, on the other hand, refers to the fact
that one (or more) color(s) are chosen reliably for an emotion
(e.g., people agree with what color(s) an emotion should be).
Previous research on color and emotion linkages tend to
come from exploring peoples’ color preferences, or peoples’
behaviors toward colored stimuli. The majority of empirical
research comes from marketing, art and design, and studies of
consumer behavior (e.g., Stone and English, 1998; Singh, 2006;
Gilbert et al., 2016). Even those within psychology, however, are
not conducted with the structure of emotion as the focus1. While
this research is undoubtedly valuable (some of which is reviewed
below), the current study is the first to use emotion theory to
explore whether colors are diagnostic of emotion categories.
According to one widely-held view of emotion, people
experience and perceive emotion because people have emotions.
According to many individual theories within this view, emotions
are treated as biological distinct entities, each with a separate
internal mechanism that gives rise to a suite of coordinated
and prescriptive reactions that define the emotion and make it
different from another emotion (e.g., behavior, facial expressions,
tone of voice, bodily changes, brain changes) (Tomkins, 1962,
1963; Izard, 1971, 1994; Ekman, 1972, 1992, 1993; Scherer, 2000;
Matsumoto et al., 2008; Brosch et al., 2010; Ekman and Cordaro,
2011; Sauter et al., 2011). As a result, a person is able to measure
behavior, facial muscle activity, vocal acoustics, autonomic
physiology, or brain activation and know what emotion it is that
(s)he or another person is experiencing. Among such emotions
are those in English we call anger, fear, etc. (Ekman, 1992).
Because in these theories emotions are thought of as biological
distinct entities, their signaling behavior is thought to provide an
honest and viable cue of the sender’s internal state (which should
predict his or her behavior and the like). Accordingly, an emotion
1The one exception was a theoretical model designed by Plutchik (1980) in which
he arranged emotions around a color wheel, which was motivated by the analogy
between the opponent–color theory and the circularity of emotions (cf. Hanada,
2017, p. 3–4). He proposed that there are eight primary emotions consisting of
four pairs of opposite emotions (joy–sadness, trust–disgust, fear–anger, surprise–
anticipate) like complementary colors. Other emotions are mixtures of primary
emotions, like other colors are mixtures of primary colors. Although he implied
similarities between the color and emotion circle, he did not seem to argue that his
emotion circle matched the color circle (cf. Hanada, 2017, p. 3–4). Interestingly,
although commonly referred to in the media and popular psychology, there is
little to no evidence that his color-emotion pairings were ever empirically tested
or supported.
would be associated with a particular color because the color
is reflected in a biological way. Color vision evolved because it
afforded survival since it likely added fitness-relevant behaviors
(Hutchings, 1997; Byrne and Hilbert, 2003). For example, the
color “red” is directly correlated with the level of testosterone in
males across many species (e.g., high-ranking males often show
more bright coloration). Accordingly, “red” would likely portray
potential threat and increased likelihood of aggression associated
with rank. Behavioral research reinforces this notion, including
increasing threat perception and dominance (Hill and Barton,
2005; Feltman and Elliot, 2011; Elliot and Maier, 2012; Young
et al., 2013). Colors such as olives and browns are most likely
to convey rotting food or feces which should be avoided, and
should therefore induce a feeling of disgust. That is, the specific
colors assigned to an emotion should be based on an evolutionary
honest signal or should at least promote evolutionary success
(Mollon, 1989; Palmer and Schloss, 2010).
Biologically-mediated relationships between wavelength of
color and arousal have long been hypothesized, and mainly
conform to the Yerkes-Dodson law of arousal-performance
(Yerkes and Dodson, 1908). For example, long wavelength colors
(e.g., “red,” “orange,” “yellow,” etc.) are more arousing than short
wavelength colors (Lewinski, 1938; Goldstein, 1942; Wilson,
1966), and therefore should increase performance. Likewise,
pleasure is associated with brighter, more saturated colors,
with the relationship tending to be curvilinear (Guilford and
Smith, 1959). A few studies provide evidence for specific colors
affecting the body’s physiological responses to arousal (i.e., skin
conductance, heart rate, and respiration) (e.g., Wilson, 1966;
Jacobs and Hustmyer Jr, 1974), yet other studies have found
reverse effects of color on arousal or no effects at all. Other
studies provide evidence that physiological effects of colors are
cognitively mediated (e.g., Kaiser, 1984; Detenber et al., 2000).
It is also possible that biologically-derived color-emotion
associations might be modified, reinforced or conventionalized
as a result of frequently-paired associations. That is, initial
tendencies can be exaggerated by the constant associations of
color-emotion within a culture. From a very young age, people
are constantly exposed to explicit and implicit pairings, and once
these pairings are learned they can be associated with knowledge
and behaviors outside the realm of consciousness (Bargh, 1990;
Elliot et al., 2007). They can also serve as unconscious primes
to automatically influence cognitive processing. Put into practice,
“red” and its association with danger, is reinforced in our culture
by the “red” of stoplights and stop signs, the “red” of fire alarms,
the “red” of pens used to correct papers, etc. (Elliot et al., 2007).
Other theories of emotion, however, do not treat emotions
as biological entities, but rather explanatory constructs that a
person evokes to explain the primitive changes in his or her own
internal sensations or such similar changes within another person
(Russell, 1980; Barrett, 2006a,b). In doing so, the individual
arrives at a diagnostic category in his or her body (or in another’s
body) using situational cues and his or her conceptual knowledge.
According to one prominent theory (Barrett, 2006a,b, 2011, 2012,
2017), an emotion is created when a person uses his or her
conceptual knowledge to label (with an emotion word) these
internal sensations to arrive at the constructed classification
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Fugate and Franco Emotion and Color
of a particular emotion2. Consequently, specific color-emotion
associations should be based on a person’s beliefs as experienced
in their culture and through their language, rather than on
evolutionary honest signals.
Survey Evidence for Color-Emotion
Associations
Despite the plethora of information available on the internet,
there is little empirical research for consistency and specificity
of color-emotion pairings. Those studies which have explored
such relationships tend to suffer from several methodological
restrictions, and/or do not look at emotions as the source of
investigation. Other studies ask participants to rate colors on a
list of emotional adjectives, which only loosely map onto some
aspects of emotion while blurring other defining features.
In the first empirical study of color and emotion, English-
speaking participants selected one of eight colored pieces of paper
for each of several emotion adjectives. Participants chose “black”
and “brown” for adjectives related to sadness, and “yellow” for
adjectives related to happiness (Wexner, 1954). Despite these
pairings, none of the colors accounted for more than 50% of
the answers, suggesting a high amount of variability among
respondents. In another classic study, English-speaking children
and college students chose “yellow,” “orange,” “green,” and “blue”
crayons to color happy images, but “red,” “brown,” and “black” to
color sad images (Cimbalo et al., 1978).
More recently, English-speaking participants saw 13 colors
(including “black”, “white”, and “gray”) and gave one emotional
adjective for each color (Kaya and Epps, 2004). The majority
of participants produced emotional adjectives related to anger
when viewing “red,” adjectives related to calmness when viewing
“blue,” those relating to fear when viewing “black,” those relating
to happy when viewing “yellow,” and adjectives related to disgust
for the color “green-yellow.” These findings are somewhat, but
not fully, consistent with another study of English-speaking
participants. Here, participants assigned “red” most often to
anger, “green” to jealousy, and “yellow” to happy, and “blue” to
sad (Sutton and Altarriba, 2016). Yet “red” was also the most
frequent color listed for contempt,fear, and surprise; and “green”
was also the most frequent color for disgust; “yellow” also for joy;
and “blue” also for pride. Therefore, in both studies there were no
consistent and specific pairings.
Despite the generalized lack of inferred consistency and
specificity, there are several other major problems with all of
these studies. First, some use color words rather than color
squares (actual colors). Obviously, what “red” a person thinks
of when asked to associate it with anything (emotion or
otherwise) is going to vary widely. Second, even the studies
which do use color squares fail to control or recognize that
2The role that language has on emotion perception has grown within the last
10 years, and many studies now show that words matter to how perceivers not
only categorize but also perceive the emotional world (For reviews, see Fugate,
2013; Lindquist and Gendron, 2013; Lindquist et al., 2015; see Fugate et al.,
2018). Accordingly, color-related terms, including basic colors within a language,
are likely to play a role on how we both perceive and link color with emotion.
For example, English has many color-emotion phrases which might influence
color-emotion pairings (e.g., “red with rage,” “green with envy,” “feeling blue”).
perceived color is determined by hue, saturation (chroma), and
brightness/lightness (HSLs) (Centore, 2012). Therefore, one hue
may be seen as several different “colors,” depending on the
saturation and brightness. In addition, the space of color is not
spherical, but rather lopsided and a partially flattened globe. The
result is that the maximum saturation for a hue near “bluish-
green” is not nearly as extreme as the maximum saturation for
hues near “red” (see D’Andrade and Egan, 1974). Many of the
color-emotion findings might therefore be due less to hue, but
rather to the degree of saturation and brightness (Whitfield and
Wiltshire, 1990). For example, “yellow” might be rated as happy
because it is often imagined (or depicted) at full saturation and
brightness (e.g., a sunny “yellow”). A mustard “yellow” at the
same hue—but one that is low in the other two properties—is
unlikely to be very happy.
Only one study has tried to explain how hue, saturation,
and brightness contribute directly to the emotional dimensions
of pleasure, arousal, and dominance (Valdez and Mehrabian,
1994). In that study, brightness (69%) and saturation (22%)
predicted pleasure, whereas saturation (60%) and darkness (31%)
mainly predicted arousal. Dominance was mainly predicted by
brightness (and to a lesser extent saturation).
Summary of Previous Studies
Although several studies associate colors with emotions (see
Table 1), the data are far from clear. Undoubtedly, the strongest
link between an individual emotion and color is “red” and anger,
which has been noted across studies and formats (e.g., Kaya
and Epps, 2004; Sutton and Altarriba, 2016). Yet, none of these
studies show that “red” is specific to anger, as it has also been
associated with love and embarrassment, especially when the
range of emotions is restricted. There is less evidence (although
some studies report a link) for “yellow” and happiness. Yet,
“yellow” has also been associated with other emotions, including
envy in some cultures. Furthermore, a handful of studies report
a link between “blue” and sadness, but other studies show that
“blue” is associated with calmness. In addition, behavioral work
shows that some color-emotion pairings might be unconscious
and can be modified by exposure to learned behaviors (e.g., Elliot
et al., 2007). Finally, the brightness of a color seems to reflect a
dimension of “activity” or “pleasure,” whereas the saturation of a
color reflects a dimension of “potency” or “arousal.”
The Current Studies
The purpose of this research was to test whether English-speaking
North Americans show consistency and specificity among color-
emotion pairings when a variety of controlled color stimuli (i.e.,
color swatches) are used. In both studies, participants (n=73
and n=52) completed an online survey in which they could
chose up to three colors to associate with a list of emotion
words. Study 2 was a replication of study 1, but added in an
additional 10 emotions for a total of 20 emotions. In addition, we
added or changed 12 colors to better represent color space (hue,
saturation, and light) for a total of 28 colors. The rationale for
study 2 was 4-fold: to address the criticisms that (1) study 1 was
underpowered, (2) that more emotions should be investigated,
(3) the choices of color should be better distributed with respect
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TABLE 1 | Summary of previous color-emotion studies by date.
Author(s) Participants No. of Colors/Emotions General results Consistency/
Specificity?
Wexner, 1954 English-speaking adults Eight colored pieces of
paper; matched to 11
emotional adjectives
“red” =related to exciting and protective;
“black” and “brown” =related to sadness;
“black” =related to powerful
“yellow” =related to cheerful; “orange” =
related to distressed; “blue” =related to
tender and secure and calm; “purple” =
related to dignified
Not tested
Adams and
Osgood, 1973
Male high school students
in 23 countries
Eight color words rated on
12 opponent word scales
Across countries: “Blue” and “green” =cool;
“red” and “yellow” =warm; Differences
across cultures noted but not discussed
Not tested
Cimbalo et al.,
1978
English-speaking children Used seven crayons to
color eight pre-determined
happy and sad images
“yellow,” “orange,” “green,” and “blue” =
happiness;
“red,” “brown,” and “black” =sadness
Not tested
Hupka et al.,
1997
Participants in five countries
(Germany, Mexico, Poland,
Russia, and the US)
Twelve color words
associated with four
emotions
Anger =“red” and “black” (Germans,
Mexicans, Russians, US); “red” and “black”
and “purple” (Poles);
Envy =“black” and “yellow” and “purple”
(Russians); “yellow” (Germans); “black” and
“purple” (Mexicans); “black” and “red” and
“green” (US); “black” and “red” (Poles);
Fear =“black” (Germans, Poles, Russians,
US); “red” and “black” (Mexicans)
Jealousy =“red” and “black” (Russians,
Mexicans, US); “red” and “yellow” (Germans),
“red” (Poland)
Madden et al.,
2000
Adults in Australia, in Brazil,
Canada, Columbia, Hong
Kong, People’s Republic of
China (PRC), Taiwan, and
the US
Ten colors on computer
screen rated on 20
opponent word scales (only
some emotional)
Across countries: “green”/“blue”/“white” =
gentle; calming; peaceful (some countries
also added in beautiful and pleasant)
“black”/”brown” sadness; stale (some
countries also added formal and masculine)
“red” =active; hot; vibrant (some countries
also added pleasant)
Not tested
Kaya and Epps,
2004
English-speaking adults Thirteen colors on computer
screen and stated how each
color felt
Only emotions same as ours listed:
(results in next column)
Anger
Annoyed
Bored
Calm
Disgust
Excited
Fearful
Happy
Loved
Sad
Inferred Consistency:
Top color % >twice
second color:
Anger =“red”;
Bored =“gray”;
Calm =“blue”;
Fear =“black”;
Happy =“yellow”;
Disgust =
“green-yellow”;
Inferred Specificity from
potentials above:
Top rated emotion % >
twice emotion (or >
50%):
“yellow” =Happy
“blue” =Calm
Gao et al., 2007 Adults in Japan, Thailand,
Hong Kong, Taiwan, Italy,
Sweden, and Spain
Two hundred and fourteen
color samples rated on 12
opponent word scales
Across cultures:
2 Major factors (82% overall variance):
Factor 1 =chroma (saturation) =
activity/excitement
Factor 2 =lightness =potency/forcefulness
Differences across cultures on emphasis of
HSL: chroma more important for Italians and
HK; hue for Japanese, Taiwanese, Swedish,
and Spanish; chroma and hue equally
important for Thai.
Not tested
(Continued)
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TABLE 1 | Continued
Author(s) Participants No. of Colors/Emotions General results Consistency/
Specificity?
Sutton and
Altarriba, 2016
English-speaking adults One hundred sixty
emotional items and listed a
color
Only emotions same as ours listed:
(results in next column)
Anger
Contempt
Disgusted
Embarrassed
Fear
Happy
Jealousy
Joy
Love
Sad
Proud (Pride)
Surprise
Shame
Inferred Consistency:
Top color % >x2
second color (or
>50%):
Anger =“red”
Embarrassed =“red”
Happy =“yellow”
Jealousy =“green”
Love =“red”
Sad =“blue”
Inferred Specificity from
potentials above:
Top rated emotion % >
x2 emotion (or >50%)
“green” =jealousy
“blue” =sad
“yellow” =happiness
Hanada, 2017 Japanese-speaking adults Forty colors on computer
screen and stated how each
color felt
“red” =Active emotions (e.g., anger, passion,
excitement);
“orange” and “yellow” =Steadier emotions
(e.g., pleasantness, happiness); “dark blue”
and “violet” =Inactive and negative emotions
(e.g., disgust, hatred, depression, and fear)
Not tested
to HSL, and (4) that participants’ perception of colors might
be different across different devices. To this last criticism, we
also conducted an additional control study in which a separate
group of participants (n=25) completed the same survey under
controlled lighting conditions on one color-calibrated monitor
(see Survey Presentation: Additional Laboratory Control).
In both studies, participants could chose up to three
colored squares for each emotion and then indicated a
numerical value as to the strength of that relationship using
a 10 point Likert scale. In study 1, participants were also
asked to pick only one, different color for each emotion to
assess the differences in a restricted vs. less-restricted setting.
Results for this “forced choice” question are discussed in
Supplemental Materials.
In agreement with theories of emotion which are constructed,
we predicted that there would be few instances in which
participants agreed with one another on what color best depicted
an emotion (i.e., consistency), and few unique pairings between
an individual color(s) and an emotion (i.e., specificity). In
addition, we predicted that any positive results would not hold
up under statistical re-sampling or with a set of participants
with minor changes in the procedure (Hypothesis 1). This would
suggest that effects which have been previously reported are
experiment-specific and not generalizable across studies when
different methodologies or participants are used.
Finally, based on some suggestion from previous literature
(Valdez and Mehrabian, 1994), we predicted that saturation and
lightness would be better predictors of the colors people associate
with an emotion than perceived color (Hypothesis 2).
Our research is novel in that it uses two theoretical tenets
inherent to emotion theory, which we precisely operationalize
and test statistically. In addition, we present a large list of
emotions, present our colors as swatches rather than color words
(paying attention to HSL), and allow participants to indicate a
strength of agreement for up to three colors for each emotion.
We also compare people’s agreement using this format with a
more traditional format in which participants were asked to
choose one color for each emotion. Finally, we bootstrapped our
data in study 1 to show that the likelihood of replicating any
individual color-emotion pairing was well-below the accepted
level of statistical significance. Study 2 confirmed the fact that any
consistent and specificity findings which occur are not stable even
among English-speakers.
METHODS
Participants: Study 1 and 2
In study 1, 74 English-speaking adults completed the online
color-emotion survey. One participant did not complete
the survey and their data were removed. Participants were
recruited from the general public via targeted email and then
through “snow-ball” sampling to complete the survey online
without identifying information. These participants were not
compensated. The survey was available on Facebook and the
PI’s personal webpage for a total of 9 months. All participants
identified as native English-speakers with high command of
the English language and resided in either America or Canada.
Five of the 74 participants had additional fluency in another
language. Among the 74 participants, 17 were recruited from the
sponsoring University. These participants received one research
credit as part of an introductory psychology class.
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In study 2, 104 native English-speakers from the United States,
Canada, and India completed a similar version of the
online survey, through Amazon Mechanical Turk (M-Turk)—
a crowdsourcing dissemination apparatus. Participants were
required to be “master-Turkers” and completed the survey on an
iPhone in which the screen brightness could be controlled (see
Survey Procedure). They received $1.00 in their Amazon account
once they completed the survey. Forty-eight native English-
speakers who completed the survey in English had additional
fluency (indicated as above a “5” of 10 on the scale) in another
language: Tamil (n=26), Arabic (n=1), Bengali (n=3),
Hindi (n=9), Vietnamese (n=1), German (n=1), and
French (n=7). In this study, we removed participants with
additional high fluency in another language(s). Four participants
did not complete the survey and their data were removed. Fifty
two participants’ data were analyzed. Forty-four percent of the
remaining participants were males, and the majority (29%) were
between 25 and 34 years old.
In the laboratory control study, an additional 25 participants
from the sponsoring University completed the same survey
under controlled lighting conditions on one color-calibrated
monitor. These participants received one research credit as part
of an introductory psychology class. Both studies, as well as the
laboratory control study, were carried out in accordance with the
recommendations of the University of MA–Dartmouth, IRB #
14.051. The protocol was approved by the A. Karberg, Director
of Institutional Compliance. All subjects gave online informed
consent in accordance with the Declaration of Helsinki.
Survey Presentation
The online survey was similar for both studies 1 and 2. The survey
in study 1 was programmed in HTML by an outside programmer
and housed on the first author’s personal, secure internet domain.
It was made available by link on the first author’s professional
website and on Facebook. The survey in study 2 was compiled
using Qualtrics because it was easily integrated into M-Turk. The
survey was advertised on M-Turk in order to recruit English-
speaking participants from a larger demographic in a shorter
period of time.
Both surveys began with directions and informed consent
(which was obtained by checking a box to continue). In addition,
in study 2, participants received a visual diagram of how to
adjust the brightness on their screen to 100% and to temporary
remove “night mode” viewing from their iPhone. These two small
changes helped to control for screen brightness which otherwise
might have differed in study 1, and affected how participants
perceived each color.
Participants in both studies were then asked the same five
demographic questions (age, biological sex, country of origin,
fluency in English, and fluency with additional languages).
Participants in study 1 advanced the survey page to next see a list
of 10 emotion words (presented randomly across participants)
with 23 colored squares (programmed in random order, but not
randomized across participants) underneath the list of emotion
words. The color swatches totaled roughly one-half of the page
and were of equal sizes, so that participants (regardless of viewing
device) could see the full list of emotion words and colors on one
page without scrolling. Participants moved one colored square to
the box next to each of the emotion words (for results of this
question, see Supplemental Materials). Participants could only
use each color once, and therefore not all colors were used.
Next, participants in both studies received the same list of
emotions (in random order for both studies) with a matrix of
all the colored squares (programmed in the same, but random
order) below each emotion word. The color squares comprised
a grid in which each color was the same size. Participants could
choose (by clicking) on as many as three colors. Once a color was
clicked, a Likert scale (1 =a small amount, 10 =a lot) appeared
prompting them to indicate the strength of this color-emotion
relationship. Participants did not need to select three colors (at
minimum they needed to select one color), and could repeat
numeric values.
Participants in both studies then completed a color preference
checklist and finally labeled the colors of the swatches (at the
survey’s end). The results of these questions are not reported
in this paper. Participants could not go back once a page was
completed, and the four basic pages of questions (demographics,
multiple choice intensity, preference, and labeling) for both
studies came up in the same order for all participants. The average
time to complete the survey was 15 min in both studies. All
participants were required to respond to every question or the
survey would indicate a missing response.
Additional Study 2 Control (Laboratory Study)
All participants completed the survey in the first author’s
laboratory, in which the ambient lighting was controlled and the
computer monitor was calibrated for each participant3.
Selection of Colors: Study 1
We considered systematic variations in color, as well as color
terms in English, and past research. We used 10 Munsell hues
(R, YR, Y, GY, G, BG, B, PB, P, RP). We chose roughly two
different lightness values and saturations at each hue. We also
included three achromatic colors: white, black and gray. Finally,
we chose the Hex Color closest to the Munsell color that was
considered “web safe,” meaning that across participants’ devices,
colors would appear the same. We use the common name for the
colors in this paper, but provide HSL, RGB, CIE (Commission
Internationale de l’Éclairage) (CIE, 1932), and HEX equivalents
(see Appendix A).
Selection of Colors: Study 2
We kept 16 colors from the original 23 colors from study 1. We
removed seven colors (chocolate, jade, sky blue, indigo, aqua,
turquoise, and violet) since they were rarely selected in study 1 or
were slightly different from the colors reported in Hanada (2017),
which was published between the time of study 1 and study 2. We
added 12 colors from previous studies: four colors from Kaya and
Epps (2004)—teal, purple, bright blue, periwinkle—and six colors
3The survey was taken on a Dell Latitude E5570 (5th Generation Intel Core
i5 processor, 16 GB of RAM, a 256GB SSD hard drive, with a 1920 ×1080
(15inch) screen display). A pantone huey (MEU101) monitor calibration device
(colorimeter) was used to calibrate the computer screen prior to each participant.
All participants completed the survey in a dark room.
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Fugate and Franco Emotion and Color
from Hanada (2017)—brown, light yellow, dark green, light blue,
dark blue, and dark purple. Finally, we added two additional
colors never used in research (dark red and light orange) to
compile an exhaustive list of all colors across the spectrum.
Overall, the 28 colors comprised ten saturations (chroma), 20
lightnesses (bright/dullness), and 23 hues.
Selection of Emotion: Study 1
We chose ten different “basic” emotions to list as words.
Although some researchers identify six basic emotions, others
recognize more (e.g., 10 or 12, Plutchik, 1980). The final emotions
were: anger,calmness,contempt,disgust,fear,envy,happiness,
jealousy,sadness, and surprise.
Selection of Emotion: Study 2
For study 2, we kept all original emotion words, but included
an additional ten emotion terms. These terms were recently
considered as “basic” by Cowen and Keltner (2017). The added
emotions were: alert, awe, boredom, disappointed, empathy, guilt,
joy, love, pride, and shame.
RESULTS
Consistency: Study 1
We identified the top-three indicated colors (for each emotion)
by frequency. To test for consistency of a color with an
emotion, we asked whether the intensities of these top-
indicated colors differed for an emotion using non-parametric
Freidman’s tests. We also reported Kendall’s W, which indicates
agreement of raters. Kendall’s Wis linearly related to the
mean value of Spearman’s rank correlation coefficient, but
makes no assumptions regarding the nature of the probability
distribution and can handle any number of distinct outcomes
(Kendall and Babington Smith, 1939). We report analyses
for consistency which did not reach statistical significance in
Supplemental Materials. Alpha for all analyses was set to 0.05.
Finally, we asked whether the intensities of top colors would
differ from those given to other emotions if we were to resample
our data. This is especially important, as null hypothesis testing
based on single distributions has many pitfalls and is slowly being
moved away from in social sciences (see Cumming, 2014 for a
review; see also Kline, 2013). In fact, APA now recognizes re-
sampling (in which bootstrapping is one technique, e.g., Efron
and Tibshirani, 2001) as a preferred method in research and
should be used when possible. To do this, for each emotion we
created a three-color matrix by intensity and then performed a
pairwise non-parametric bootstrap for the probability that the
intensity of the color on the row was greater than the color on
the column of the matrix. The number of samples which went
into the bootstrapping process was 10,0004.
4The standard parametric approach to hypothesis testing can only quantify
whether the mean intensity rating for one color is significantly greater than
the mean intensity rating for another color. Instead, we used a non-parametric
bootstrapping method to more precisely quantify the probability that a given
intensity rating for “red” is significantly greater than a given intensity rating for
“black”. We first sampled with replacement 10,000 samples from the intensity
ratings for “red” and another 10,000 samples from the intensity rating for “black”.
Anger
“Red” was the most frequently chosen color, followed by “black”
and then “gray” (Figure 1). The intensity for the color “red”
was high, whereas the intensities for “black” and “gray” were
moderate and low, respectively (Table 2). The intensities among
the three colors differed, χ2(2,N=73) =97.87, p<0.001, as did the
intensities among all three colors at follow-up, p<0.001 level.
There was high agreement among raters, Kendall’s W=0.670.
While these analyses suggest that “red” might be specific to anger,
resampling the data showed that the probability of replicating
this finding would be <95%: We found that the probability of
selecting a sample in which the intensity of “red” exceeded that
of “black” was around 60% (CI = −4:7) 5, whereas the probability
of selecting a sample in which the intensity of “red” exceeded that
of “gray” was about 78% (CI = −3:6). The probability of selecting
a sample in which the intensity of “black” exceeded “gray” was
about 57% (CI = −5:6). Therefore, on average, we would expect
to find the intensity of “red” to exceed that of “black” 60% of the
time. Using an alpha of 0.05 as a level of statistical significance
would require finding the effect 95% of the time. Clearly, this
falls short.
Fear
“Black” was the most frequently picked color, followed by “red”
and then “gray” (Figure 1). “Black” had a high intensity, whereas
“red” and “gray” had low intensities (Table 2). The intensities
among the three colors were significant, χ2
(2,N=73) =38.82,
p<0.001, and the intensities between “black” and “red” were
also significant, p<0.001 level. There was only low-moderate
agreement among raters, however, Kendall’s W=0.266. Again,
resampling our data showed that this was an unlikely result. The
probability of selecting a sample in which the intensity of “black”
exceeded that of “red” was about 54% (CI = −5:8), whereas
obtaining a sample in which the intensity of “black” exceeded that
of “gray” was only slightly higher, 62% (CI = −5:8). Therefore,
despite the fact that the intensity of “black” was high and differed
from that of “red,” resampling the same data suggested that we
would find black to be consistently paired with fear about half
the time.
Happiness
“Yellow” was the most frequently picked color, followed by
“sky blue” and then “aqua” (Figure 1). “Yellow” had a high
intensity, whereas “sky blue” and “aqua” had more moderate
intensities (Table 2). The intensities among the three colors were
significant χ2(2,N=73) =18.78, p<0.001, as was the difference
between “yellow” and “sky blue,” p<0.001. There was low-
moderate agreement among raters, however, Kendall’s W=
0.129. Again, bootstrapping showed that this effect was unlikely:
The likelihood that we would obtain a sample in which the
intensity of “yellow” exceeded that of “sky blue” and “aqua” was
We then computed 10,000 difference pairs by subtracting away the intensity for
“black” from that of “red”. The fraction of the difference pairs greater than zero
is the desired probability. In this case, probabilities smaller than 0.95 are not
significant.
5Our confidence intervals are based on intensity ratings, which ranged between 1
and 10.
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Fugate and Franco Emotion and Color
FIGURE 1 | Frequency of selection of colors for each emotion: study 1. Colors represent the actual color swatches. Labels for these colors are used in the next table.
See Appendix A to associate the color name with each color.
about 51% (CI = −3:6) and 44% (CI = −3:8), respectively. All
other probabilities from resampling the intensities of the top
three colors were <50%. Therefore, despite intensity analyses
which showed that the intensity of “yellow” differed from that
given to the other colors, re-sampling our data suggested that this
would be likely about 50% of the time.
Sadness
“Gray” was the most frequent color indicated for sadness,
followed by “indigo” and then “black” (Figure 1). The intensities
for all three colors were moderate (Table 2). The intensities
among the three colors differed only marginally, χ2(2,N=73) =
5.82, p=0.05. Despite this, the agreement among participants
was extremely low, Kendall’s W=0.040. This was confirmed by
the low probabilities of replicating this result when resampled
our data: All resampling probabilities were <46%. Therefore,
we conclude that “gray” is not likely to be consistently paired
with sadness.
Summary Consistency
Four of the 10 emotions showed statistical evidence for
consistency, yet agreement among raters in all conditions was
low to moderate. The exception was “red” with anger, in
which the agreement was relatively high. It is also worth
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Fugate and Franco Emotion and Color
TABLE 2 | Raw mean intensity (followed by SD) of the top colors selected for study 1.
Color
emotion
“Black” “Red” “Gray” “Yellow” “Light
Purple”
“Sky
Blue”
“Jade” “Green” “Aqua” “Indigo” “Blue” “Bright
Pink”
“Chocolate” “Dark
Yellow”
“Light
Green”
Anger 4.5
4.2
8.6
2.7
1.0
2.6
X X X X X X 0.5
1.9
–
–
X X X X
Calmness X X X X 2.3
3.7
3.1
4.4
X X 1.6
3.4
X 2.4
3.7
X X X X
Contempt 1.7
3.4
X 1.0
2.5
X 1.2
2.9
X X 0.5
1.9
X X 0.5
2.0
0.6
2.0
0.7
2.1
0.8
2.4
X
Disgust X X X X X X 0.5
1.9
0.5
1.7
X X –
–
–
–
3.6
4.1
3.2
3.7
3.1
3.9
Envy X 1.7
3.3
X X X X 2.7
4.1
3.0
4.2
X X –
–
X X X 1.4
3.1
Fear 5.7
4.3
2.5
3.7
1.6
3.2
1.0
2.7
X X X X X 0.9
2.5
X X X X X
Happiness X X X 5.3
4.6
X 2.6
4.0
X X 2.3
4.0
X 0.6
2.2
1.4
3.0
–
–
X X
Jealousy X 2.6
3.7
X X X X 2.4
4.0
2.3
4.0
X X – X X X 1.4
3.2
Sadness 2.4
4.0
X 4.2
4.2
X X X X X X 3.4
4.0
X X 0.8
2.3
X X
Surprise X X X 2.6
3.9
0.9
2.3
0.6
2.1
X X 2.1
3.4
X X 2.6
3.9
–
–
X –
–
Only colors listed which were among the top three for any emotion are listed. X indicates not compared because color was not among the top three by frequency for that emotion.
noting that when we analyzed the frequencies from the
“forced choice” question (see Supplemental Materials), we
found statistical evidence for three of the four emotions
listed above. The only pairing which did not replicate
was sadness.
Yet, when we resampled our data, even “red” being selected
as the most intense color for anger was not likely to replicate:
Rather we would expect this effect to occur about 60% of
the time, well below the 95% used in statistical significance
testing. These numbers are reflected in the discrepancies among
the color-emotion literature. Individual studies that do suggest
consistent pairings typically restrain participants’ choices and/or
use categorical assignments (e.g., yes, no). Study 2 shows that
this is the case, as when the context of the study changes (by
adding more emotions and color), these consistency pairings do
not replicate.
Specificity: Study 1
We followed similar logic as above, except that we performed
the analyses by color (rather than by emotion). Four of the 23
colors were shared among the top three choices for at least two
emotions. An additional five colors were only selected among
the top three for one emotion, but we tested them against
the second and third most frequently chosen emotions (e.g.,
“indigo,” “bright pink,” “chocolate,” “dark yellow,” and “light
green”). We did not analyze the remaining colors because they
were never in the top three for any emotion, and participants
rarely chose them. We analyzed each color separately. We
report colors that did not meet statistical significance in
Supplemental Materials.
“Red”
“Red” was indicated among the top three colors for anger,
followed by jealousy,fear, and envy, respectively (Figure 2). The
intensity of “red” for anger was high, whereas the intensity for
“red” given to the other emotions was low-moderate (Table 2).
The intensities of “red” among the emotions differed, χ2
(3, N=73) =
99.22, p<0.001, as did the intensities of “red” between anger and
jealousy,p<0.001. The agreement among raters was relatively
high, Kendall’s W=0.680. While these analyses suggest that
“red” might be specific to anger, resampling the data showed
that the probability of replicating this finding was well below
the level of statistical acceptance. The probability of selecting a
sample in which the intensity of “red” to anger exceeded that
given to fear was around 62% (CI = −4:9). All other probabilities
of selecting a sample in which the intensity of “red” was greater
for one emotion compared to that of another emotion were
<44%. Therefore, despite frequency and intensity analyses which
showed that the intensity of “red” assigned to anger differed from
jealousy, re-sampling the same data suggested that we would
expect to find this effect only about 62% of the time.
“Gray”
“Gray” was indicated among the top three colors for sadness,
fear, and contempt (Figure 2). The intensity of “gray” for sadness
was moderate, whereas the intensity of “gray” given to the other
emotions was low (Table 2). The intensities of “gray” among
the emotions differed, χ2
(3, N=73) =26.95, p<0.001, as did the
intensities between sadness and fear,p=0.01. The agreement
among raters was low, however, Kendall’s W=0.185. When
we resampled our data, the probability of selecting a sample
in which the intensity of “gray” to sadness exceeded that given
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Fugate and Franco Emotion and Color
FIGURE 2 | Frequency of selection of emotions for each color: study 1.
to fear was about 52% (CI = −6:8). All other probabilities of
selecting a sample in which the intensity of “gray” was greater
for one emotion compared to another were <46%. Therefore,
we conclude the likelihood that “gray” is specific to sadness is
quite low.
“Yellow”
“Yellow” was indicated among the top three colors for only
two emotions, happiness and surprise (Figure 2). The intensity
of “yellow” was high for happiness, and moderate for surprise
(Table 2). The intensity of “yellow” given to happiness vs. that
given to surprise differed, p<0.05. The agreement among
raters was low-moderate, however, Kendall W=0.238. Using
bootstrapping, we found that the probability of selecting a sample
in which the intensity of “yellow” given to happiness exceeded
that to surprise was about 63% (CI = −3:6). Therefore, the
likelihood that yellow is specific to happiness is quite low.
“Green”
“Green” was indicated among the top three colors for two
emotions, envy and jealousy (Figure 2). The intensity of
“green” to envy and jealousy were both moderate (Table 2).
The intensities of “green” between envy and jealousy differed,
p<0.05. The agreement among raters was low, however,
Kendall’s W=0.148. Again, when we resampled our data, we
found that the probability of selecting a sample in which the
intensity of “green” given to envy exceeded that for jealousy was
only about 25% (CI = −6:6). Therefore, we conclude that it is
unlikely that “green” is specific to envy.
“Indigo”
“Indigo” was indicated among the top three colors for only one
emotion, sadness, making it potentially specific (Figure 2). The
intensity of “indigo” was moderate for sadness (Table 2), and the
agreement among raters was low-medium, Kendall’s W=0.243.
Using bootstrapping, we found that the probability of selecting
a sample in which the intensity of “indigo” given to sadness
exceeded the intensity of “indigo” to another emotion was always
<62%. Therefore, it is unlikely that “blue” is specific to sadness,
even though participants never picked “indigo” as one of the top
three colors for any other emotion.
“Bright Pink”
“Bright pink” was indicated among the top three colors for only
one emotion, surprise, which suggested it might be specific to
it (Figure 2). The intensity of “bright pink” was moderate to
surprise (Table 2), yet the agreement among raters was also low,
Kendall’s W=0.103. Using bootstrapping, we found that the
probability of selecting a sample in which the intensity of “bright
pink” to surprise exceeded that to another emotion was always
<63%. Therefore, it is unlikely that “bright pink” is specific to
surprise, even though participants never picked “bright pink” as
one of the top three colors for any other emotion.
“Chocolate,” “Dark Yellow,” and “Light Green”
“Chocolate,” “dark yellow,” and “light green” were only indicated
among the top three colors for one emotion, disgust (Figure 2).
This might suggest that these three colors are specific to this
emotion. Yet, when we looked at the intensities of each color
given to disgust, they were all moderate (Table 2), and the
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Fugate and Franco Emotion and Color
agreement among individuals for each color ranged from low to
moderate, Kendall’s W=0.227, 0.285, 0.076, respectively.
Using bootstrapping, we found that the probability of selecting
a sample in which the intensity of “chocolate” to disgust exceeded
that to another emotion was always <58%. Likewise, the
probability of selecting a sample in which the intensity of “dark
yellow” to disgust exceeded that to another emotion was always
<55%. Finally, the probability of selecting a sample in which the
intensity of “light green” exceeded that for any other emotion was
always <59%. Therefore, none of these colors are likely to be
specific to disgust, despite participants only picking these three
colors as the top colors for disgust.
Summary Specificity
Nine of the 23 colors showed some evidence of specificity, yet
agreement among raters in all conditions were low to moderate.
The exception was “red” with anger, in which the agreement was
high. Yet, when we resampled our data, even “red” as specific to
anger was not likely to replicate. Study 2 shows that this is the
case, as only one of the specificity pairings replicated.
It is also worth noting that when we analyzed the frequencies
from the “forced choice” question (see Supplemental Materials),
we found statistical evidence for six colors listed above. More
specifically, we did not replicate the effects of “chocolate” and
“gray,” yet we did find specificity for “jade” and “black” which we
had not before. This was likely because “jade” was now specific
to envy (“green,” which was also high for envy, was now specific
to jealousy), and “black” was now specific to fear (“red,” which
was also high for fear, was now specific to anger). Finally, “bright
pink” was specific, but now with calmness (not surprise)! These
results show how restraining the choice of colors (to one color
vs. several colors) can profoundly affect the pairings that emerge,
suggesting that not only are individual color-emotion pairings
likely specific to one experiment, but also to how (within the same
experiment) the question is asked.
Consistency: Study 2
We used the same analyses as in study 1 for consistency. We did
not bootstrap our data in this study since the primary purposes
was to show that effects which do emerge within the context of
an experiment are typically experiment-specific, and not likely to
replicate in another. We report analyses for consistency which
did not reach statistical significance in Supplemental Materials.
Alpha for all analyses was set to 0.05.
Disappointment
“Gray” was the top-ranked color for the emotion disappointment,
followed by “black” and “dark yellow,” respectively (Figure 3).
Overall, participants assigned different intensities to the top
three-ranked colors, χ2
(2, N=52) =16.00, p<0.001. In addition,
the intensities assigned to “gray” statistically differed from
intensities assigned to “black,” p=0.05, yet there was low
agreement among individuals, Kendall’s W=0.154 (Table 3).
Love
“Red” was the top-ranked color for the emotion love, followed
by “light red” and “pink,” respectively (Figure 3). Overall,
FIGURE 3 | Frequency of selection of colors for emotion: study 2. Colors
represent the actual color swatches. See Appendix A to associate the color
name with each color. Colors with <5% frequency rating are not depicted in
pie charts.
participants gave different intensities to the top three-ranked
colors, χ2
(2, N=52) =11.06, p=0.01. In addition, the intensities
assigned to “red” were statistically different from “light red,” p=
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Fugate and Franco Emotion and Color
TABLE 3 | Raw mean intensity (followed by SD) of the top colors selected for study 2.
Color
emotion
“Black” “Red” “Gray” “Yellow” “Light
Purple”
“Dark
Red”
“Orange” “Green” “Brown” “Dark
Blue”
“Blue” “Bright
Pink”
“Bright
Green”
“Dark
Yellow”
“Pink” “White” “Periwinkle” “Light
Blue”
“Dark
Green”
“Bright
Blue”
Light
Red”
“Light
Green”
Alert X 3.5
4.4
X 3.6
4.43
X X 1.8
3.6
XXXXXXXXX X XXXXX
Anger 2.1
3.7
6.4
4.3
X X X 5.2
4.5
XXXXXXXXXX X XXXXX
Awe X X X X X X X X X X X 1.2
3.1
1.1
2.9
X 1.4
3.1
X X X X X X X
Boredom 2.1
3.6
X 4.3
4.5
XXXXXXXXXXXX2.7
4
X X X X X X
Calmness X X X X X X X X X X 2.1
3.6
X X X X X 2.9
4.2 2.1
3.6
X X X X
Contempt X 2.2
3.7
1.6
3.2
X X 1.5
3.4
XXXXXXXXXX X XXXXX
Disappointment 2.5
3.9
X 4
4.4
XXXXXXXXXX1.3
2.9
X X X X X X X X
Disgust 1.6
3.2
XXXXXXX
1.5
3.4
X X X X 2.2
3.7
X X X X X X X X
Empathy X X X X 1.8
3.4
X X X X X X X X X 1.7
3.3
X X 1.1
2.8
X X X X
Envy X X X X X X X 4.5
4.5
X X X X 3.4
4.1
X X X X X 2.1
3.9
X X X
Fear 4.1
4.6
2.2
3.8 1.6
3.5
XXXXXXXXXXXXX X XXXXX
Guilt 2.3
4.1
X 2
3.6
X X X X X 1.1
2.8
XXXXXXX X XXXXX
Happy X X X 3.5
4.6
XXXXXXXX2
3.5
X X X X X X 1.4
3.2
X X
Jealousy X X X X X X X 2.8
4
X X X X 2.1
3.7
XXX X XXXX
2.3
3.5
Joy X X X 3.1
4.3
X X X X X X X X 2.1
3.7
X 1.9
3.7
X X X X X X X
Love X 4.8
4.8
XXXXXXXXXXXX3
4.1
X X X X X 3.1
4.3
X
Pride X 1.7
3.5
XXXXXXX
1.6
3.5
XXXXXX X XX
1.8
3.6
X X
Sad 2.4
4.1
X 3.5
4.3
X X X X X X 1.7
3.4
XXXXXX X XXXXX
Shame 2.9
4.3
X 2.4
3.8
XXXXXXXXXX1.2
2.9
X X X X X X X X
Surprise X X X 3.1
4.2
X X X X X X X 1.6
3.4
2
3.6
XXX X XXXXX
Only colors listed which were among the top three for any emotion are listed. X indicates not compared because color was not among the top three by frequency for that emotion.
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Fugate and Franco Emotion and Color
0.04, yet there was low agreement among individuals, Kendall’s
W=0.106 (Table 3).
Summary Consistency
Only love and “red,” and disappointment and “gray” were
consistent pairings in study 2. Neither of these emotions
were included in study 1. Of the four emotions for which
we found consistent pairings in study 1 (anger,envy,
fear,happy), none of these replicated, although the top-
indicated color (e.g., “red,” “green,” “black,” and “yellow,”
respectively) were the same top-indicated colors in this study for
these emotions.
To address the concerns that participants perceived the
colors differently since they could use their own device (despite
our efforts to limit the survey to users of the iPhone and
instruct participants to adjust their screen brightness), we
compared the top-indicated color for each emotion from
study 2 to the lab control. For 13 of the 20 emotions,
participants indicated the same top color for both studies.
For the remaining seven emotions, what was the top color
for study 2 was either second or third (or vice versa) in
the lab control. We interpret this to mean that participants
did not perceive the colors on their devices differently
despite their ability to complete the survey in any lightning
conditions. The amount of small variability we found was
still qualitatively less than between the main results of studies
1 and 2.
Specificity: Study 2
We used the same analyses as in study 1 for specificity. Again,
we did not bootstrap the data. As in study 1, we analyzed each
color separately. We report colors that did not meet statistical
significance in Supplemental Materials.
“Dark Red”
Anger was the top ranked emotion for the color “dark red,”
followed by love, and contempt, respectively (Figure 4). Overall,
participants assigned different intensities to the top three-ranked
emotions, χ2
(2, N=52) =18.37, p<0.001, and the intensities
assigned to anger statistically differed from intensities assigned
to love,p=0.01. Overall, however, agreement among individuals
was low, Kendall’s W=0.177 (Table 3).
“Green”
Envy was the top ranked emotion for the color “green,” followed
by jealousy, and calmness, respectively (Figure 4). Overall,
participants assigned different “green” color intensities to the
top three-ranked emotions, χ2
(2, N=52) =23.15, p<0.001. In
addition, intensities associated with envy significantly differed
from intensities associated with jealousy,p=0.04, and agreement
was moderate, Kendall’s W=0.223 (Table 3).
“Light Red”
Love was the top ranked emotion for the color “light red,”
followed by surprise and joy, respectively (Figure 4). Participants
gave different intensities to the top three-ranked emotions,
χ2
(2, N=52) =15.39, p<0.001. In addition, intensities assigned
FIGURE 4 | Frequency of selection of emotions for each color: study 2.
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Fugate and Franco Emotion and Color
to love statistically differed from intensities assigned to surprise,
p=0.04, yet the agreement among individuals was still low,
Kendall’s W=0.148 (Table 3).
Summary Specificity
Three of the 28 colors showed statistical specificity: “dark red”
was specific to anger, “green” with envy, and “light red” with love.
Nonetheless, agreement among raters in all conditions was low
to moderate. Of the specific pairings in study 1, only “green” with
envy replicated.
HSL Dimensions: Study 2
Finally, to test our hypothesis (Hypothesis 2) that the individual
facets of color (HSL) would better predict the color-emotion
pairings than the perceived color (referred to by the color name),
we ran a series of nominal (categorical) regressions. First, we ran
a categorical regression using HSL combined as predictors with
emotion as the target. Then we ran the same regression with color
(referred to by color name) as the predictor. Finally, we looked
at how the model changed with HSL added in individually. This
allowed us to get a sense of which facet predicted the most
agreement. From this we could compare with previous studies
that suggested lightness and saturation are mainly responsible for
emotion-color pairings.
We found that HSL together produced a significant model,
F(5, 50) =52.15, p<0.001, r2=0.84 (standardized B = −0.80,
p=0.008, hue; standardized B = −0.21, p=0.080, saturation;
standardized B = −0.15, p=0.145, lightness). Although the
model was significant, only hue was a significant predictor. This
was confirmed when we tested each predictor separately: The
model was significant with hue, F(1, 54) =156.56, p<0.001,
r2=0.74, but also with only saturation, F(1, 80) =14.38, p<
0.001, r2=0.15. Lightness remained non-significant, p=0.080.
Color (as named) did not produce a significant model, F(2, 81)
=2.06, p=0.134, r2=0.05. These findings confirm our
second hypothesis that individual facets of color, namely hue and
saturation, predicted agreement of emotion assignment better
than the perceived color. Unlike other previous studies (e.g.,
Valdez and Mehrabian, 1994; Gao et al., 2007), however, lightness
did not have an effect, whereas hue did have an effect.
DISCUSSION
An evolutionary approach to emotion is consistent with the idea
that there should be specificity and consistency for color-emotion
pairings. Consistency suggests that an emotion is reliably paired
with a color(s). Specificity suggests that a color(s) is specific to an
emotion.
We performed two studies, in which English-speaking
participants completed a similar online survey assessing their
color-emotion pairing by selecting up to three colors for each
emotion and indicating an intensity. In study 2, we added more
emotions and colors. We also conducted a laboratory control
study in which new participants completed the survey under
controlled lighting conditions and the color on the computer
monitor was calibrated for each participant. Study 1 had the
additional unique question of asking participants to pick one,
different emotion for each emotion, in a “forced choice” format
which is more similar to the previous research on this topic.
In study 1, we found that only four emotions showed any
evidence of color consistency, and only nine colors showed
any evidence of emotion specificity. When we resampled our
data, however, there was no evidence that any of these effects
would be retained. Bootstrapping revealed no evidence that
this would replicate with statistical certainty, consistent with
our hypothesis that individual color-emotion pairings are due
to the experimental context and not universal or stable across
participants and formats. Interestingly, and perhaps more telling,
is that when participants in study 1 were forced to choose only
one color, “green” was now specific to jealousy, emphasizing the
instability of color-emotion pairings when only the formatting of
the question changes.
It is important to note that bootstrapping, which is now
considered a “gold standard” (if not a necessity) in data
collection, provides the ability to draw hundreds or thousands
of samples from the sample distribution rather than relying on
only the sample distribution. To this end, the standard error from
resampling is a much better estimate of the standard deviation.
The results are comparable to performing the experiment
hundreds of thousands of times on the same participants.
In study 2, we found only two emotions that showed any
evidence of color consistency, and only three colors that showed
any evidence of emotion specificity. In addition, the individual
pairings of consistency and specificity we found for study 1
were different from those in study 2, with the exception that
“green” was specific to envy in both studies. We also confirmed
that participants’ judgments are not influenced by perceiving the
colors differently based on the device on which they take the
survey, since the top-indicated color was the same across the
majority of emotions between the laboratory control study and
the results reported herein.
Without strong evidence for consistency and specificity for
emotion-color pairings, why does it feel as if anger is “red” and
that envy is “green”? One reason is that previous investigations
often severely limit the range of answer choices which imposes
consistency (and erroneously is used as an indication of the
existence of a diagnostic signal) (for more on this, see Barrett,
2006a,b). Said another way, the context created by the study can
inflate agreement. Therefore, when like-minded people are asked
to associate emotions with colors from a restrictive set, they are
likely to use the provided answer choices and verbal labels to
make “best guesses.”
Another reason is that our folk psychology—that which comes
from our culture and is reinforced by our culture (i.e., popular
movies and books which continue to portray such pairings)—
reinforces these relationships. For example, in many American
films, “good guys” are often dressed in white and “bad guys”
in black (Frank and Gilovich, 1988; Meier et al., 2004). In
Hellenistic, Roman, and Christian traditions, “white” is the color
of joy, innocence, and purity, whereas “black” is the color
associated with evil (e.g., “Satan is the Prince of Darkness” and
“Jesus is the Light of the World”) (Meier et al., 2007; Chiou and
Cheng, 2013; see Meier et al., 2004). Allah is equated with light
in the Koran, and truth is characterized as a “light” or “lamp”
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Fugate and Franco Emotion and Color
in Buddhist writings (Meier et al., 2004). Metaphors not only
communicate abstract concepts, but they might also be necessary
for grounding them as well (Gibbs, 1992; Lakoff and Johnson,
1999; Lakens et al., 2012, 2013). This is true of emotion, too (e.g.,
Barrett et al., 2007). In English, we have sayings such as: “hot-
headed” or “red in the face” to refer to anger, “feeling blue” to
refer to sadness, and “the green-eyed monster” to refer to envy.
According to The Theory of Constructed Emotion (Barrett,
2017), as a child learns more emotion words, s(he) becomes better
at perceiving emotion and shows more granular categories. In
this view, emotion is no different from other abstract categories
which are learned during development. Emotion words help
to create discrete emotion categories because they help to
activate situated conceptualizations which might increase the
processing of sensory information that is consistent with such
conceptualizations (Barrett, 2006a,b; Wilson-Mendenhall et al.,
2011; Lindquist and Gendron, 2013; see Lupyan and Ward, 2013;
Fugate and Barrett, 2014; Lindquist et al., 2015).
We believe this to be similar for color language. For example,
when people are asked to think about color-emotion pairings,
they reference language. For instance, a person might reference
linguistic phrases and metaphors that help clarify what color
anger should be (e.g., “red in the face” or “seeing red”, Fetterman
et al., 2012). They might also access the learned relationships
between “red” and warning signals (e.g., fire alarms, stop signs,
red ink), which highlight the need for vigilance and avoidance
which are shared aspects with “anger” (see Elliot et al., 2007).
Admittedly, the purpose of this paper was not to test the reasons
behind any potential pairings, as much as it was to see whether
such pairings exist and whether colors are specific to emotions.
Future research should indeed further explore the reasons behind
beliefs in color-emotion associations as well as explore differences
in color-emotion agreement cross-culturally.
Although study 2 addressed several limitations of study 1,
there remain several possible influences on color-emotion
pairings which we could not or did not control. For example, even
though we included more colors and more emotions in study 2,
there are over 1,800 notations listed in the Munsell book of colors.
We also included emotions which have been referenced in the
psychological literature as “potentially basic”: We did not use
emotional adjectives as some previous studies have done.
While there are some shortcomings to this research, we
believe that the strengths lie in deriving our hypotheses with
respect to emotion theory which we statistically test. In addition,
we present our colors as swatches rather than color words
(paying attention to HSL), and allow participants to indicate a
strength of agreement for up to three colors for each emotion.
We also compare people’s agreement using this format with a
more traditional format in which participants were asked to
choose one color for each emotion. Finally, we bootstrapped our
data in study 1 to show that the likelihood of replicating any
individual color-emotion pairing was well-below the accepted
level of statistical significance.
AUTHOR CONTRIBUTIONS
JF was involved in all aspects of the research. CF helped
disseminate the survey, reviewed the literature, prepared data
for analysis, analyzed data, and aided in figure preparation.
She was also responsible for most aspects of study 2. Both
authors approved the final revised manuscript for submission.
The authors declare no conflicts of interest. Portions of this
data were presented at the Psychonomics conference in 2015
and at the American Psychological Association conference
in 2018.
ACKNOWLEDGMENTS
We would like to thank A. Scott McCauley, who helped
develop the survey. We would also like to thank, Tuan
Le Mau, who performed the bootstrapping analyses
in Python.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fpsyg.
2019.00206/full#supplementary-material
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Conflict of Interest Statement: The authors declare that the research was
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